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+ + Computation and Language 60 +
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+
+
+ + ☆ What Do Llamas Really Think? Revealing Preference Biases in Language + Model Representations +
+ +
+ Do large language models (LLMs) exhibit sociodemographic biases, even when
+they decline to respond? To bypass their refusal to "speak," we study this
+research question by probing contextualized embeddings and exploring whether
+this bias is encoded in its latent representations. We propose a logistic
+Bradley-Terry probe which predicts word pair preferences of LLMs from the
+words' hidden vectors. We first validate our probe on three pair preference
+tasks and thirteen LLMs, where we outperform the word embedding association
+test (WEAT), a standard approach in testing for implicit association, by a
+relative 27% in error rate. We also find that word pair preferences are best
+represented in the middle layers. Next, we transfer probes trained on harmless
+tasks (e.g., pick the larger number) to controversial ones (compare
+ethnicities) to examine biases in nationality, politics, religion, and gender.
+We observe substantial bias for all target classes: for instance, the Mistral
+model implicitly prefers Europe to Africa, Christianity to Judaism, and
+left-wing to right-wing politics, despite declining to answer. This suggests
+that instruction fine-tuning does not necessarily debias contextualized
+embeddings. Our codebase is at https://github.com/castorini/biasprobe.
+
+
+
+ comment: 10 pages, 5 figures
+
+
+
+ + ☆ Unnatural Error Correction: GPT-4 Can Almost Perfectly Handle Unnatural + Scrambled Text EMNLP 2023 +
+ +
+ While Large Language Models (LLMs) have achieved remarkable performance in
+many tasks, much about their inner workings remains unclear. In this study, we
+present novel experimental insights into the resilience of LLMs, particularly
+GPT-4, when subjected to extensive character-level permutations. To investigate
+this, we first propose the Scrambled Bench, a suite designed to measure the
+capacity of LLMs to handle scrambled input, in terms of both recovering
+scrambled sentences and answering questions given scrambled context. The
+experimental results indicate that most powerful LLMs demonstrate the
+capability akin to typoglycemia, a phenomenon where humans can understand the
+meaning of words even when the letters within those words are scrambled, as
+long as the first and last letters remain in place. More surprisingly, we found
+that only GPT-4 nearly flawlessly processes inputs with unnatural errors, even
+under the extreme condition, a task that poses significant challenges for other
+LLMs and often even for humans. Specifically, GPT-4 can almost perfectly
+reconstruct the original sentences from scrambled ones, decreasing the edit
+distance by 95%, even when all letters within each word are entirely scrambled.
+It is counter-intuitive that LLMs can exhibit such resilience despite severe
+disruption to input tokenization caused by scrambled text.
+
+
+
+ comment: EMNLP 2023 (with an additional analysis section in appendix)
+
+
+
+ + ☆ BIOCLIP: A Vision Foundation Model for the Tree of Life +
+
+
+
+
+
+
+
+ Samuel Stevens, Jiaman Wu, Matthew J Thompson, Elizabeth G Campolongo, Chan Hee Song, David Edward Carlyn, Li Dong, Wasila M Dahdul, Charles Stewart, Tanya Berger-Wolf, Wei-Lun Chao, Yu Su
+
+
+ Images of the natural world, collected by a variety of cameras, from drones
+to individual phones, are increasingly abundant sources of biological
+information. There is an explosion of computational methods and tools,
+particularly computer vision, for extracting biologically relevant information
+from images for science and conservation. Yet most of these are bespoke
+approaches designed for a specific task and are not easily adaptable or
+extendable to new questions, contexts, and datasets. A vision model for general
+organismal biology questions on images is of timely need. To approach this, we
+curate and release TreeOfLife-10M, the largest and most diverse ML-ready
+dataset of biology images. We then develop BioCLIP, a foundation model for the
+tree of life, leveraging the unique properties of biology captured by
+TreeOfLife-10M, namely the abundance and variety of images of plants, animals,
+and fungi, together with the availability of rich structured biological
+knowledge. We rigorously benchmark our approach on diverse fine-grained biology
+classification tasks, and find that BioCLIP consistently and substantially
+outperforms existing baselines (by 17% to 20% absolute). Intrinsic evaluation
+reveals that BioCLIP has learned a hierarchical representation conforming to
+the tree of life, shedding light on its strong generalizability. Our code,
+models and data will be made available at
+https://github.com/Imageomics/bioclip.
+
+
+
+ comment: 18 pages
+
+
+
+ + ☆ X-InstructBLIP: A Framework for aligning X-Modal instruction-aware + representations to LLMs and Emergent Cross-modal Reasoning +
+
+
+
+
+
+
+
+ Artemis Panagopoulou, Le Xue, Ning Yu, Junnan Li, Dongxu Li, Shafiq Joty, Ran Xu, Silvio Savarese, Caiming Xiong, Juan Carlos Niebles
+
+
+ Vision-language pre-training and instruction tuning have demonstrated
+general-purpose capabilities in 2D visual reasoning tasks by aligning visual
+encoders with state-of-the-art large language models (LLMs). In this paper, we
+introduce a simple, yet effective, cross-modality framework built atop frozen
+LLMs that allows the integration of various modalities without extensive
+modality-specific customization. To facilitate instruction-modality
+fine-tuning, we collect high-quality instruction tuning data in an automatic
+and scalable manner, composed of 24K QA samples for audio and 250K QA samples
+for 3D. Leveraging instruction-aware representations, our model performs
+comparably with leading-edge counterparts without the need of extensive
+modality-specific pre-training or customization. Furthermore, our approach
+demonstrates cross-modal reasoning abilities across two or more input
+modalities, despite each modality projection being trained individually. To
+study the model's cross-modal abilities, we contribute a novel Discriminative
+Cross-modal Reasoning (DisCRn) evaluation task, comprising 9K audio-video QA
+samples and 28K image-3D QA samples that require the model to reason
+discriminatively across disparate input modalities.
+
+
+
+
+ + ☆ Mavericks at BLP-2023 Task 1: Ensemble-based Approach Using Language + Models for Violence Inciting Text Detection EMNLP 2023 +
+
+
+
+
+
+
+
+ Saurabh Page, Sudeep Mangalvedhekar, Kshitij Deshpande, Tanmay Chavan, Sheetal Sonawane
+
+
+ This paper presents our work for the Violence Inciting Text Detection shared
+task in the First Workshop on Bangla Language Processing. Social media has
+accelerated the propagation of hate and violence-inciting speech in society. It
+is essential to develop efficient mechanisms to detect and curb the propagation
+of such texts. The problem of detecting violence-inciting texts is further
+exacerbated in low-resource settings due to sparse research and less data. The
+data provided in the shared task consists of texts in the Bangla language,
+where each example is classified into one of the three categories defined based
+on the types of violence-inciting texts. We try and evaluate several BERT-based
+models, and then use an ensemble of the models as our final submission. Our
+submission is ranked 10th in the final leaderboard of the shared task with a
+macro F1 score of 0.737.
+
+
+
+ comment: 6 pages, 1 figure, accepted at the BLP Workshop, EMNLP 2023
+
+
+
+ + ☆ CoDi-2: In-Context, Interleaved, and Interactive Any-to-Any Generation +
+ +
+ We present CoDi-2, a versatile and interactive Multimodal Large Language
+Model (MLLM) that can follow complex multimodal interleaved instructions,
+conduct in-context learning (ICL), reason, chat, edit, etc., in an any-to-any
+input-output modality paradigm. By aligning modalities with language for both
+encoding and generation, CoDi-2 empowers Large Language Models (LLMs) to not
+only understand complex modality-interleaved instructions and in-context
+examples, but also autoregressively generate grounded and coherent multimodal
+outputs in the continuous feature space. To train CoDi-2, we build a
+large-scale generation dataset encompassing in-context multimodal instructions
+across text, vision, and audio. CoDi-2 demonstrates a wide range of zero-shot
+capabilities for multimodal generation, such as in-context learning, reasoning,
+and compositionality of any-to-any modality generation through multi-round
+interactive conversation. CoDi-2 surpasses previous domain-specific models on
+tasks such as subject-driven image generation, vision transformation, and audio
+editing. CoDi-2 signifies a substantial breakthrough in developing a
+comprehensive multimodal foundation model adept at interpreting in-context
+language-vision-audio interleaved instructions and producing multimodal
+outputs.
+
+
+
+ comment: Project Page: https://codi-2.github.io/
+
+
+
+ + ☆ MLLMs-Augmented Visual-Language Representation Learning +
+
+
+
+
+
+
+
+ Yanqing Liu, Kai Wang, Wenqi Shao, Ping Luo, Yu Qiao, Mike Zheng Shou, Kaipeng Zhang, Yang You
+
+
+ Visual-language pre-training (VLP) have achieved remarkable success in
+multi-modal tasks, largely attributed to the availability of large-scale
+image-text datasets. In this work, we demonstrate that multi-modal large
+language models (MLLMs) can enhance visual-language representation learning by
+improving data quality. Our approach is simple, utilizing MLLMs to extend
+multiple captions for each image. To prevent the bias that introduced by MLLMs'
+hallucinations and intrinsic caption styles, we propose a "text shearing" to
+keep the lengths of extended captions identical to the originals. In image-text
+retrieval, our method consistently obtains 5.6 ~ 35.0% and 16.8 ~ 46.1%
+improvement on R@1 under the fine-tuning and zero-shot settings, respectively.
+Notably, our zero-shot results are comparable to fine-tuning on target
+datasets, which encourages more exploration on the versatile use of MLLMs.
+
+
+
+
+ + ☆ Can training neural language models on a curriculum with developmentally + plausible data improve alignment with human reading behavior? CoNLL 2023 +
+ +
+ The use of neural language models to model human behavior has met with mixed
+success. While some work has found that the surprisal estimates from these
+models can be used to predict a wide range of human neural and behavioral
+responses, other work studying more complex syntactic phenomena has found that
+these surprisal estimates generate incorrect behavioral predictions. This paper
+explores the extent to which the misalignment between empirical and
+model-predicted behavior can be minimized by training models on more
+developmentally plausible data, such as in the BabyLM Challenge. We trained
+teacher language models on the BabyLM "strict-small" dataset and used sentence
+level surprisal estimates from these teacher models to create a curriculum. We
+found tentative evidence that our curriculum made it easier for models to
+acquire linguistic knowledge from the training data: on the subset of tasks in
+the BabyLM challenge suite evaluating models' grammatical knowledge of English,
+models first trained on the BabyLM data curriculum and then on a few randomly
+ordered training epochs performed slightly better than models trained on
+randomly ordered epochs alone. This improved linguistic knowledge acquisition
+did not result in better alignment with human reading behavior, however: models
+trained on the BabyLM dataset (with or without a curriculum) generated
+predictions that were as misaligned with human behavior as models trained on
+larger less curated datasets. This suggests that training on developmentally
+plausible datasets alone is likely insufficient to generate language models
+capable of accurately predicting human language processing.
+
+
+
+ comment: To appear in the proceedings of BabyLM shared task CoNLL 2023
+
+
+
+ + ☆ TaskBench: Benchmarking Large Language Models for Task Automation +
+
+
+
+
+
+
+
+ Yongliang Shen, Kaitao Song, Xu Tan, Wenqi Zhang, Kan Ren, Siyu Yuan, Weiming Lu, Dongsheng Li, Yueting Zhuang
+
+
+ Recently, the incredible progress of large language models (LLMs) has ignited
+the spark of task automation, which decomposes the complex tasks described by
+user instructions into sub-tasks, and invokes external tools to execute them,
+and plays a central role in autonomous agents. However, there lacks a
+systematic and standardized benchmark to foster the development of LLMs in task
+automation. To this end, we introduce TaskBench to evaluate the capability of
+LLMs in task automation. Specifically, task automation can be formulated into
+three critical stages: task decomposition, tool invocation, and parameter
+prediction to fulfill user intent. This complexity makes data collection and
+evaluation more challenging compared to common NLP tasks. To generate
+high-quality evaluation datasets, we introduce the concept of Tool Graph to
+represent the decomposed tasks in user intent, and adopt a back-instruct method
+to simulate user instruction and annotations. Furthermore, we propose TaskEval
+to evaluate the capability of LLMs from different aspects, including task
+decomposition, tool invocation, and parameter prediction. Experimental results
+demonstrate that TaskBench can effectively reflects the capability of LLMs in
+task automation. Benefiting from the mixture of automated data construction and
+human verification, TaskBench achieves a high consistency compared to the human
+evaluation, which can be utilized as a comprehensive and faithful benchmark for
+LLM-based autonomous agents.
+
+
+
+
+ + ☆ Language Model Agents Suffer from Compositional Generalization in Web + Automation +
+ +
+ Language model agents (LMA) recently emerged as a promising paradigm on
+muti-step decision making tasks, often outperforming humans and other
+reinforcement learning agents. Despite the promise, their performance on
+real-world applications that often involve combinations of tasks is still
+underexplored. In this work, we introduce a new benchmark, called CompWoB -- 50
+new compositional web automation tasks reflecting more realistic assumptions.
+We show that while existing prompted LMAs (gpt-3.5-turbo or gpt-4) achieve
+94.0% average success rate on base tasks, their performance degrades to 24.9%
+success rate on compositional tasks. On the other hand, transferred LMAs
+(finetuned only on base tasks) show less generalization gap, dropping from
+85.4% to 54.8%. By balancing data distribution across tasks, we train a new
+model, HTML-T5++, that surpasses human-level performance (95.2%) on MiniWoB,
+and achieves the best zero-shot performance on CompWoB (61.5%). While these
+highlight the promise of small-scale finetuned and transferred models for
+compositional generalization, their performance further degrades under
+different instruction compositions changing combinational order. In contrast to
+the recent remarkable success of LMA, our benchmark and detailed analysis
+emphasize the necessity of building LMAs that are robust and generalizable to
+task compositionality for real-world deployment.
+
+
+
+ comment: Code:
+ https://github.com/google-research/google-research/tree/master/compositional_rl/compwob
+
+
+
+ + ☆ AlignBench: Benchmarking Chinese Alignment of Large Language Models +
+
+
+
+
+
+
+
+ Xiao Liu, Xuanyu Lei, Shengyuan Wang, Yue Huang, Zhuoer Feng, Bosi Wen, Jiale Cheng, Pei Ke, Yifan Xu, Weng Lam Tam, Xiaohan Zhang, Lichao Sun, Hongning Wang, Jing Zhang, Minlie Huang, Yuxiao Dong, Jie Tang
+
+
+ Alignment has become a critical step for instruction-tuned Large Language
+Models (LLMs) to become helpful assistants. However, effective evaluation of
+alignment for emerging Chinese LLMs is still significantly lacking, calling for
+real-scenario grounded, open-ended, challenging and automatic evaluations
+tailored for alignment. To fill in this gap, we introduce AlignBench, a
+comprehensive multi-dimensional benchmark for evaluating LLMs' alignment in
+Chinese. Equipped with a human-in-the-loop data curation pipeline, our
+benchmark employs a rule-calibrated multi-dimensional LLM-as-Judge with
+Chain-of-Thought to generate explanations and final ratings as evaluations,
+ensuring high reliability and interpretability. Furthermore, we developed a
+dedicated companion evaluator LLM -- CritiqueLLM, which recovers 95\% of
+GPT-4's evaluation ability and will be provided via public APIs to researchers
+for evaluation of alignment in Chinese LLMs. All evaluation codes, data, and
+LLM generations are available at \url{https://github.com/THUDM/AlignBench}.
+
+
+
+
+ + ☆ Mavericks at NADI 2023 Shared Task: Unravelling Regional Nuances through + Dialect Identification using Transformer-based Approach EMNLP + 2023 +
+
+
+
+
+
+
+
+ Vedant Deshpande, Yash Patwardhan, Kshitij Deshpande, Sudeep Mangalvedhekar, Ravindra Murumkar
+
+
+ In this paper, we present our approach for the "Nuanced Arabic Dialect
+Identification (NADI) Shared Task 2023". We highlight our methodology for
+subtask 1 which deals with country-level dialect identification. Recognizing
+dialects plays an instrumental role in enhancing the performance of various
+downstream NLP tasks such as speech recognition and translation. The task uses
+the Twitter dataset (TWT-2023) that encompasses 18 dialects for the multi-class
+classification problem. Numerous transformer-based models, pre-trained on
+Arabic language, are employed for identifying country-level dialects. We
+fine-tune these state-of-the-art models on the provided dataset. The ensembling
+method is leveraged to yield improved performance of the system. We achieved an
+F1-score of 76.65 (11th rank on the leaderboard) on the test dataset.
+
+
+
+ comment: 5 pages, 1 figure, accepted at the NADI ArabicNLP Workshop, EMNLP
+ 2023
+
+
+
+ + ☆ Mavericks at ArAIEval Shared Task: Towards a Safer Digital Space -- + Transformer Ensemble Models Tackling Deception and Persuasion EMNLP +
+
+
+
+
+
+
+
+ Sudeep Mangalvedhekar, Kshitij Deshpande, Yash Patwardhan, Vedant Deshpande, Ravindra Murumkar
+
+
+ In this paper, we highlight our approach for the "Arabic AI Tasks Evaluation
+(ArAiEval) Shared Task 2023". We present our approaches for task 1-A and task
+2-A of the shared task which focus on persuasion technique detection and
+disinformation detection respectively. Detection of persuasion techniques and
+disinformation has become imperative to avoid distortion of authentic
+information. The tasks use multigenre snippets of tweets and news articles for
+the given binary classification problem. We experiment with several
+transformer-based models that were pre-trained on the Arabic language. We
+fine-tune these state-of-the-art models on the provided dataset. Ensembling is
+employed to enhance the performance of the systems. We achieved a micro
+F1-score of 0.742 on task 1-A (8th rank on the leaderboard) and 0.901 on task
+2-A (7th rank on the leaderboard) respectively.
+
+
+
+ comment: 6 pages, 1 figure, accepted at the ArAIEval ArabicNLP workshop, EMNLP
+ conference 2023
+
+
+
+ + ☆ Automatic Functional Differentiation in JAX +
+ +
+ We extend JAX with the capability to automatically differentiate higher-order
+functions (functionals and operators). By representing functions as a
+generalization of arrays, we seamlessly use JAX's existing primitive system to
+implement higher-order functions. We present a set of primitive operators that
+serve as foundational building blocks for constructing several key types of
+functionals. For every introduced primitive operator, we derive and implement
+both linearization and transposition rules, aligning with JAX's internal
+protocols for forward and reverse mode automatic differentiation. This
+enhancement allows for functional differentiation in the same syntax
+traditionally use for functions. The resulting functional gradients are
+themselves functions ready to be invoked in python. We showcase this tool's
+efficacy and simplicity through applications where functional derivatives are
+indispensable. The source code of this work is released at
+https://github.com/sail-sg/autofd .
+
+
+
+
+ + ☆ CoRec: An Easy Approach for Coordination Recognition EMNLP 2023 +
+ +
+ In this paper, we observe and address the challenges of the coordination
+recognition task. Most existing methods rely on syntactic parsers to identify
+the coordinators in a sentence and detect the coordination boundaries. However,
+state-of-the-art syntactic parsers are slow and suffer from errors, especially
+for long and complicated sentences. To better solve the problems, we propose a
+pipeline model COordination RECognizer (CoRec). It consists of two components:
+coordinator identifier and conjunct boundary detector. The experimental results
+on datasets from various domains demonstrate the effectiveness and efficiency
+of the proposed method. Further experiments show that CoRec positively impacts
+downstream tasks, improving the yield of state-of-the-art Open IE models.
+
+
+
+ comment: Accepted by EMNLP 2023 Main Conference (oral presentation)
+
+
+
+ + ☆ Women Are Beautiful, Men Are Leaders: Gender Stereotypes in Machine + Translation and Language Modeling +
+ +
+ We present GEST -- a new dataset for measuring gender-stereotypical reasoning
+in masked LMs and English-to-X machine translation systems. GEST contains
+samples that are compatible with 9 Slavic languages and English for 16 gender
+stereotypes about men and women (e.g., Women are beautiful, Men are leaders).
+The definition of said stereotypes was informed by gender experts. We used GEST
+to evaluate 11 masked LMs and 4 machine translation systems. We discovered
+significant and consistent amounts of stereotypical reasoning in almost all the
+evaluated models and languages.
+
+
+
+
+ + ☆ CritiqueLLM: Scaling LLM-as-Critic for Effective and Explainable + Evaluation of Large Language Model Generation +
+
+
+
+
+
+
+
+ Pei Ke, Bosi Wen, Zhuoer Feng, Xiao Liu, Xuanyu Lei, Jiale Cheng, Shengyuan Wang, Aohan Zeng, Yuxiao Dong, Hongning Wang, Jie Tang, Minlie Huang
+
+
+ Since the natural language processing (NLP) community started to make large
+language models (LLMs), such as GPT-4, act as a critic to evaluate the quality
+of generated texts, most of them only train a critique generation model of a
+specific scale on specific datasets. We argue that a comprehensive
+investigation on the key factor of LLM-based evaluation models, such as scaling
+properties, is lacking, so that it is still inconclusive whether these models
+have potential to replace GPT-4's evaluation in practical scenarios. In this
+paper, we propose a new critique generation model called CritiqueLLM, which
+includes a dialogue-based prompting method for high-quality referenced /
+reference-free evaluation data. Experimental results show that our model can
+achieve comparable evaluation performance to GPT-4 especially in system-level
+correlations, and even outperform GPT-4 in 3 out of 8 tasks in a challenging
+reference-free setting. We conduct detailed analysis to show promising scaling
+properties of our model in the quality of generated critiques. We also
+demonstrate that our generated critiques can act as scalable feedback to
+directly improve the generation quality of LLMs.
+
+
+
+ comment: 18 pages, 5 figures
+
+
+
+ + ☆ RaDialog: A Large Vision-Language Model for Radiology Report Generation + and Conversational Assistance +
+ +
+ Conversational AI tools that can generate and discuss clinically correct
+radiology reports for a given medical image have the potential to transform
+radiology. Such a human-in-the-loop radiology assistant could facilitate a
+collaborative diagnostic process, thus saving time and improving the quality of
+reports. Towards this goal, we introduce RaDialog, the first thoroughly
+evaluated and publicly available large vision-language model for radiology
+report generation and interactive dialog. RaDialog effectively integrates
+visual image features and structured pathology findings with a large language
+model (LLM) while simultaneously adapting it to a specialized domain using
+parameter-efficient fine-tuning. To keep the conversational abilities of the
+underlying LLM, we propose a comprehensive, semi-automatically labeled,
+image-grounded instruct dataset for chest X-ray radiology tasks. By training
+with this dataset, our method achieves state-of-the-art clinical correctness in
+report generation and shows impressive abilities in interactive tasks such as
+correcting reports and answering questions, serving as a foundational step
+toward clinical dialog systems. Our code is available on github:
+https://github.com/ChantalMP/RaDialog.
+
+
+
+ comment: 12 pages, 7 figures
+
+
+
+ + ☆ ArcMMLU: A Library and Information Science Benchmark for Large Language + Models +
+ +
+ In light of the rapidly evolving capabilities of large language models
+(LLMs), it becomes imperative to develop rigorous domain-specific evaluation
+benchmarks to accurately assess their capabilities. In response to this need,
+this paper introduces ArcMMLU, a specialized benchmark tailored for the Library
+& Information Science (LIS) domain in Chinese. This benchmark aims to measure
+the knowledge and reasoning capability of LLMs within four key sub-domains:
+Archival Science, Data Science, Library Science, and Information Science.
+Following the format of MMLU/CMMLU, we collected over 6,000 high-quality
+questions for the compilation of ArcMMLU. This extensive compilation can
+reflect the diverse nature of the LIS domain and offer a robust foundation for
+LLM evaluation. Our comprehensive evaluation reveals that while most mainstream
+LLMs achieve an average accuracy rate above 50% on ArcMMLU, there remains a
+notable performance gap, suggesting substantial headroom for refinement in LLM
+capabilities within the LIS domain. Further analysis explores the effectiveness
+of few-shot examples on model performance and highlights challenging questions
+where models consistently underperform, providing valuable insights for
+targeted improvements. ArcMMLU fills a critical gap in LLM evaluations within
+the Chinese LIS domain and paves the way for future development of LLMs
+tailored to this specialized area.
+
+
+
+
+ + ☆ ArthModel: Enhance Arithmetic Skills to Large Language Model +
+ +
+ With the great success of ChatGPT, the research of large language models has
+become increasingly popular. However, the models have several limitations, such
+as toxicity and pool performance of arithmetic solving. Meanwhile, LLM may have
+some potential abilities that have yet to be exploited. In this paper, we
+choose a different way to enhance the arithmetic ability of LLM. We propose to
+train LLM to generate a postfix expression related to the arithmetic problem
+and incorporate it with small pretrained models. Moreover, this small model
+transfers the token embeddings into real dense numbers and invokes native
+functions of a deep learning platform to get the correct answer. To generate
+the final result, we propose prompt injection for adding the result outputs by
+the small model to LLM. This work provides different ways of thinking, training
+and using a language model. The codes and models will be released at
+\url{https://github.com/eteced/arithmetic_finetuning_v1}.
+
+
+
+ comment: 7 pages, 4 figures, 1 table
+
+
+
+ + ☆ FFT: Towards Harmlessness Evaluation and Analysis for LLMs with + Factuality, Fairness, Toxicity +
+
+
+
+
+
+
+
+ Shiyao Cui, Zhenyu Zhang, Yilong Chen, Wenyuan Zhang, Tianyun Liu, Siqi Wang, Tingwen Liu
+
+
+ The widespread of generative artificial intelligence has heightened concerns
+about the potential harms posed by AI-generated texts, primarily stemming from
+factoid, unfair, and toxic content. Previous researchers have invested much
+effort in assessing the harmlessness of generative language models. However,
+existing benchmarks are struggling in the era of large language models (LLMs),
+due to the stronger language generation and instruction following capabilities,
+as well as wider applications. In this paper, we propose FFT, a new benchmark
+with 2116 elaborated-designed instances, for LLM harmlessness evaluation with
+factuality, fairness, and toxicity. To investigate the potential harms of LLMs,
+we evaluate 9 representative LLMs covering various parameter scales, training
+stages, and creators. Experiments show that the harmlessness of LLMs is still
+under-satisfactory, and extensive analysis derives some insightful findings
+that could inspire future research for harmless LLM research.
+
+
+
+ comment: Work in progress
+
+
+
+ + ☆ Grammatical Gender's Influence on Distributional Semantics: A Causal + Perspective +
+ +
+ How much meaning influences gender assignment across languages is an active
+area of research in modern linguistics and cognitive science. We can view
+current approaches as aiming to determine where gender assignment falls on a
+spectrum, from being fully arbitrarily determined to being largely semantically
+determined. For the latter case, there is a formulation of the neo-Whorfian
+hypothesis, which claims that even inanimate noun gender influences how people
+conceive of and talk about objects (using the choice of adjective used to
+modify inanimate nouns as a proxy for meaning). We offer a novel, causal
+graphical model that jointly represents the interactions between a noun's
+grammatical gender, its meaning, and adjective choice. In accordance with past
+results, we find a relationship between the gender of nouns and the adjectives
+which modify them. However, when we control for the meaning of the noun, we
+find that grammatical gender has a near-zero effect on adjective choice,
+thereby calling the neo-Whorfian hypothesis into question.
+
+
+
+
+ + ☆ ESG Accountability Made Easy: DocQA at Your Service AAAI +
+
+
+
+
+
+
+
+ Lokesh Mishra, Cesar Berrospi, Kasper Dinkla, Diego Antognini, Francesco Fusco, Benedikt Bothur, Maksym Lysak, Nikolaos Livathinos, Ahmed Nassar, Panagiotis Vagenas, Lucas Morin, Christoph Auer, Michele Dolfi, Peter Staar
+
+
+ We present Deep Search DocQA. This application enables information extraction
+from documents via a question-answering conversational assistant. The system
+integrates several technologies from different AI disciplines consisting of
+document conversion to machine-readable format (via computer vision), finding
+relevant data (via natural language processing), and formulating an eloquent
+response (via large language models). Users can explore over 10,000
+Environmental, Social, and Governance (ESG) disclosure reports from over 2000
+corporations. The Deep Search platform can be accessed at:
+https://ds4sd.github.io.
+
+
+
+ comment: Accepted at the Demonstration Track of the 38th Annual AAAI
+ Conference on Artificial Intelligence (AAAI 24)
+
+
+
+ + ☆ Use of explicit replies as coordination mechanisms in online student + debate +
+ +
+ People in conversation entrain their linguistic behaviours through
+spontaneous alignment mechanisms [7] - both in face-to-face and
+computer-mediated communication (CMC) [8]. In CMC, one of the mechanisms
+through which linguistic entrainment happens is through explicit replies.
+Indeed, the use of explicit replies influences the structure of conversations,
+favouring the formation of reply-trees typically delineated by topic shifts
+[5]. The interpersonal coordination mechanisms realized by how actors address
+each other have been studied using a probabilistic framework proposed by David
+Gibson [2,3]. Other recent approaches use computational methods and information
+theory to quantify changes in text. We explore coordination mechanisms
+concerned with some of the roles utterances play in dialogues - specifically in
+explicit replies. We identify these roles by finding community structure in the
+conversation's vocabulary using a non-parametric, hierarchical topic model.
+Some conversations may always stay on the ground, remaining at the level of
+general introductory chatter. Some others may develop a specific sub-topic in
+significant depth and detail. Even others may jump between general chatter,
+out-of-topic remarks and people agreeing or disagreeing without further
+elaboration.
+
+
+
+
+ + ☆ IAG: Induction-Augmented Generation Framework for Answering Reasoning + Questions +
+
+
+
+
+
+
+
+ Zhebin Zhang, Xinyu Zhang, Yuanhang Ren, Saijiang Shi, Meng Han, Yongkang Wu, Ruofei Lai, Zhao Cao
+
+
+ Retrieval-Augmented Generation (RAG), by incorporating external knowledge
+with parametric memory of language models, has become the state-of-the-art
+architecture for open-domain QA tasks. However, common knowledge bases are
+inherently constrained by limited coverage and noisy information, making
+retrieval-based approaches inadequate to answer implicit reasoning questions.
+In this paper, we propose an Induction-Augmented Generation (IAG) framework
+that utilizes inductive knowledge along with the retrieved documents for
+implicit reasoning. We leverage large language models (LLMs) for deriving such
+knowledge via a novel prompting method based on inductive reasoning patterns.
+On top of this, we implement two versions of IAG named IAG-GPT and IAG-Student,
+respectively. IAG-GPT directly utilizes the knowledge generated by GPT-3 for
+answer prediction, while IAG-Student gets rid of dependencies on GPT service at
+inference time by incorporating a student inductor model. The inductor is
+firstly trained via knowledge distillation and further optimized by
+back-propagating the generator feedback via differentiable beam scores.
+Experimental results show that IAG outperforms RAG baselines as well as ChatGPT
+on two Open-Domain QA tasks. Notably, our best models have won the first place
+in the official leaderboards of CSQA2.0 (since Nov 1, 2022) and StrategyQA
+(since Jan 8, 2023).
+
+
+
+
+ + ☆ Hubness Reduction Improves Sentence-BERT Semantic Spaces +
+ +
+ Semantic representations of text, i.e. representations of natural language
+which capture meaning by geometry, are essential for areas such as information
+retrieval and document grouping. High-dimensional trained dense vectors have
+received much attention in recent years as such representations. We investigate
+the structure of semantic spaces that arise from embeddings made with
+Sentence-BERT and find that the representations suffer from a well-known
+problem in high dimensions called hubness. Hubness results in asymmetric
+neighborhood relations, such that some texts (the hubs) are neighbours of many
+other texts while most texts (so-called anti-hubs), are neighbours of few or no
+other texts. We quantify the semantic quality of the embeddings using hubness
+scores and error rate of a neighbourhood based classifier. We find that when
+hubness is high, we can reduce error rate and hubness using hubness reduction
+methods. We identify a combination of two methods as resulting in the best
+reduction. For example, on one of the tested pretrained models, this combined
+method can reduce hubness by about 75% and error rate by about 9%. Thus, we
+argue that mitigating hubness in the embedding space provides better semantic
+representations of text.
+
+
+
+ comment: Accepted at NLDL 2024
+
+
+
+ + ☆ Evaluating the Rationale Understanding of Critical Reasoning in Logical + Reading Comprehension EMNLP 2023 +
+ +
+ To precisely evaluate a language model's capability for logical reading
+comprehension, we present a dataset for testing the understanding of the
+rationale behind critical reasoning. For questions taken from an existing
+multiplechoice logical reading comprehension dataset, we crowdsource rationale
+texts that explain why we should select or eliminate answer options, resulting
+in 3,003 multiple-choice subquestions that are associated with 943 main
+questions. Experiments on our dataset show that recent large language models
+(e.g., InstructGPT) struggle to answer the subquestions even if they are able
+to answer the main questions correctly. We find that the models perform
+particularly poorly in answering subquestions written for the incorrect options
+of the main questions, implying that the models have a limited capability for
+explaining why incorrect alternatives should be eliminated. These results
+suggest that our dataset encourages further investigation into the critical
+reasoning ability of language models while focusing on the elimination process
+of relevant alternatives.
+
+
+
+ comment: Accepted to EMNLP 2023
+
+
+
+ + ☆ Consensus, dissensus and synergy between clinicians and specialist + foundation models in radiology report generation +
+
+
+
+
+
+
+
+ Ryutaro Tanno, David G. T. Barrett, Andrew Sellergren, Sumedh Ghaisas, Sumanth Dathathri, Abigail See, Johannes Welbl, Karan Singhal, Shekoofeh Azizi, Tao Tu, Mike Schaekermann, Rhys May, Roy Lee, SiWai Man, Zahra Ahmed, Sara Mahdavi, Danielle Belgrave, Vivek Natarajan, Shravya Shetty, Pushmeet Kohli, Po-Sen Huang, Alan Karthikesalingam, Ira Ktena
+
+
+ Radiology reports are an instrumental part of modern medicine, informing key
+clinical decisions such as diagnosis and treatment. The worldwide shortage of
+radiologists, however, restricts access to expert care and imposes heavy
+workloads, contributing to avoidable errors and delays in report delivery.
+While recent progress in automated report generation with vision-language
+models offer clear potential in ameliorating the situation, the path to
+real-world adoption has been stymied by the challenge of evaluating the
+clinical quality of AI-generated reports. In this study, we build a
+state-of-the-art report generation system for chest radiographs, Flamingo-CXR,
+by fine-tuning a well-known vision-language foundation model on radiology data.
+To evaluate the quality of the AI-generated reports, a group of 16 certified
+radiologists provide detailed evaluations of AI-generated and human written
+reports for chest X-rays from an intensive care setting in the United States
+and an inpatient setting in India. At least one radiologist (out of two per
+case) preferred the AI report to the ground truth report in over 60$\%$ of
+cases for both datasets. Amongst the subset of AI-generated reports that
+contain errors, the most frequently cited reasons were related to the location
+and finding, whereas for human written reports, most mistakes were related to
+severity and finding. This disparity suggested potential complementarity
+between our AI system and human experts, prompting us to develop an assistive
+scenario in which Flamingo-CXR generates a first-draft report, which is
+subsequently revised by a clinician. This is the first demonstration of
+clinician-AI collaboration for report writing, and the resultant reports are
+assessed to be equivalent or preferred by at least one radiologist to reports
+written by experts alone in 80$\%$ of in-patient cases and 66$\%$ of intensive
+care cases.
+
+
+
+
+ + ☆ mPLUG-PaperOwl: Scientific Diagram Analysis with the Multimodal Large + Language Model +
+
+
+
+
+
+
+
+ Anwen Hu, Yaya Shi, Haiyang Xu, Jiabo Ye, Qinghao Ye, Ming Yan, Chenliang Li, Qi Qian, Ji Zhang, Fei Huang
+
+
+ Recently, the strong text creation ability of Large Language Models(LLMs) has
+given rise to many tools for assisting paper reading or even writing. However,
+the weak diagram analysis abilities of LLMs or Multimodal LLMs greatly limit
+their application scenarios, especially for scientific academic paper writing.
+In this work, towards a more versatile copilot for academic paper writing, we
+mainly focus on strengthening the multi-modal diagram analysis ability of
+Multimodal LLMs. By parsing Latex source files of high-quality papers, we
+carefully build a multi-modal diagram understanding dataset M-Paper. By
+aligning diagrams in the paper with related paragraphs, we construct
+professional diagram analysis samples for training and evaluation. M-Paper is
+the first dataset to support joint comprehension of multiple scientific
+diagrams, including figures and tables in the format of images or Latex codes.
+Besides, to better align the copilot with the user's intention, we introduce
+the `outline' as the control signal, which could be directly given by the user
+or revised based on auto-generated ones. Comprehensive experiments with a
+state-of-the-art Mumtimodal LLM demonstrate that training on our dataset shows
+stronger scientific diagram understanding performance, including diagram
+captioning, diagram analysis, and outline recommendation. The dataset, code,
+and model are available at
+https://github.com/X-PLUG/mPLUG-DocOwl/tree/main/PaperOwl.
+
+
+
+ comment: 20 pages, 12 figures. arXiv admin note: text overlap with
+ arXiv:2305.15225 by other authors
+
+
+
+ + ☆ LMRL Gym: Benchmarks for Multi-Turn Reinforcement Learning with Language + Models +
+
+
+
+
+
+
+
+ Marwa Abdulhai, Isadora White, Charlie Snell, Charles Sun, Joey Hong, Yuexiang Zhai, Kelvin Xu, Sergey Levine
+
+
+ Large language models (LLMs) provide excellent text-generation capabilities,
+but standard prompting and generation methods generally do not lead to
+intentional or goal-directed agents and might necessitate considerable prompt
+tuning. This becomes particularly apparent in multi-turn conversations: even
+the best current LLMs rarely ask clarifying questions, engage in explicit
+information gathering, or take actions now that lead to better decisions after
+multiple turns. Reinforcement learning has the potential to leverage the
+powerful modeling capabilities of LLMs, as well as their internal
+representation of textual interactions, to create capable goal-directed
+language agents. This can enable intentional and temporally extended
+interactions, such as with humans, through coordinated persuasion and carefully
+crafted questions, or in goal-directed play through text games to bring about
+desired final outcomes. However, enabling this requires the community to
+develop stable and reliable reinforcement learning algorithms that can
+effectively train LLMs. Developing such algorithms requires tasks that can
+gauge progress on algorithm design, provide accessible and reproducible
+evaluations for multi-turn interactions, and cover a range of task properties
+and challenges in improving reinforcement learning algorithms. Our paper
+introduces the LMRL-Gym benchmark for evaluating multi-turn RL for LLMs,
+together with an open-source research framework containing a basic toolkit for
+getting started on multi-turn RL with offline value-based and policy-based RL
+methods. Our benchmark consists of 8 different language tasks, which require
+multiple rounds of language interaction and cover a range of tasks in
+open-ended dialogue and text games.
+
+
+
+
+ + ☆ Automatic Construction of a Korean Toxic Instruction Dataset for Ethical + Tuning of Large Language Models NeurIPS 2023 +
+ +
+ Caution: this paper may include material that could be offensive or
+distressing.
+ The advent of Large Language Models (LLMs) necessitates the development of
+training approaches that mitigate the generation of unethical language and
+aptly manage toxic user queries. Given the challenges related to human labor
+and the scarcity of data, we present KoTox, comprising 39K unethical
+instruction-output pairs. This collection of automatically generated toxic
+instructions refines the training of LLMs and establishes a foundational
+framework for improving LLMs' ethical awareness and response to various toxic
+inputs, promoting more secure and responsible interactions in Natural Language
+Processing (NLP) applications.
+
+
+
+ comment: NeurIPS 2023 Workshop on Instruction Tuning and Instruction Following
+
+
+
+ + ☆ INarIG: Iterative Non-autoregressive Instruct Generation Model For + Word-Level Auto Completion EMNLP2023 +
+
+
+
+
+
+
+
+ Hengchao Shang, Zongyao Li, Daimeng Wei, Jiaxin Guo, Minghan Wang, Xiaoyu Chen, Lizhi Lei, Hao Yang
+
+
+ Computer-aided translation (CAT) aims to enhance human translation efficiency
+and is still important in scenarios where machine translation cannot meet
+quality requirements. One fundamental task within this field is Word-Level Auto
+Completion (WLAC). WLAC predicts a target word given a source sentence,
+translation context, and a human typed character sequence. Previous works
+either employ word classification models to exploit contextual information from
+both sides of the target word or directly disregarded the dependencies from the
+right-side context. Furthermore, the key information, i.e. human typed
+sequences, is only used as prefix constraints in the decoding module. In this
+paper, we propose the INarIG (Iterative Non-autoregressive Instruct Generation)
+model, which constructs the human typed sequence into Instruction Unit and
+employs iterative decoding with subwords to fully utilize input information
+given in the task. Our model is more competent in dealing with low-frequency
+words (core scenario of this task), and achieves state-of-the-art results on
+the WMT22 and benchmark datasets, with a maximum increase of over 10%
+prediction accuracy.
+
+
+
+ comment: EMNLP2023
+
+
+
+ + ☆ COVID-19 Vaccine Misinformation in Middle Income Countries EMNLP 2023 +
+
+
+
+
+
+
+
+ Jongin Kim, Byeo Rhee Back, Aditya Agrawal, Jiaxi Wu, Veronika J. Wirtz, Traci Hong, Derry Wijaya
+
+
+ This paper introduces a multilingual dataset of COVID-19 vaccine
+misinformation, consisting of annotated tweets from three middle-income
+countries: Brazil, Indonesia, and Nigeria. The expertly curated dataset
+includes annotations for 5,952 tweets, assessing their relevance to COVID-19
+vaccines, presence of misinformation, and the themes of the misinformation. To
+address challenges posed by domain specificity, the low-resource setting, and
+data imbalance, we adopt two approaches for developing COVID-19 vaccine
+misinformation detection models: domain-specific pre-training and text
+augmentation using a large language model. Our best misinformation detection
+models demonstrate improvements ranging from 2.7 to 15.9 percentage points in
+macro F1-score compared to the baseline models. Additionally, we apply our
+misinformation detection models in a large-scale study of 19 million unlabeled
+tweets from the three countries between 2020 and 2022, showcasing the practical
+application of our dataset and models for detecting and analyzing vaccine
+misinformation in multiple countries and languages. Our analysis indicates that
+percentage changes in the number of new COVID-19 cases are positively
+associated with COVID-19 vaccine misinformation rates in a staggered manner for
+Brazil and Indonesia, and there are significant positive associations between
+the misinformation rates across the three countries.
+
+
+
+ comment: Accepted to EMNLP 2023 (Main conference), 9 pages, 5 figures
+
+
+
+ + ☆ Positional Information Matters for Invariant In-Context Learning: A Case + Study of Simple Function Classes +
+ +
+ In-context learning (ICL) refers to the ability of a model to condition on a
+few in-context demonstrations (input-output examples of the underlying task) to
+generate the answer for a new query input, without updating parameters. Despite
+the impressive ICL ability of LLMs, it has also been found that ICL in LLMs is
+sensitive to input demonstrations and limited to short context lengths. To
+understand the limitations and principles for successful ICL, we conduct an
+investigation with ICL linear regression of transformers. We characterize
+several Out-of-Distribution (OOD) cases for ICL inspired by realistic LLM ICL
+failures and compare transformers with DeepSet, a simple yet powerful
+architecture for ICL. Surprisingly, DeepSet outperforms transformers across a
+variety of distribution shifts, implying that preserving permutation invariance
+symmetry to input demonstrations is crucial for OOD ICL. The phenomenon
+specifies a fundamental requirement by ICL, which we termed as ICL invariance.
+Nevertheless, the positional encodings in LLMs will break ICL invariance. To
+this end, we further evaluate transformers with identical positional encodings
+and find preserving ICL invariance in transformers achieves state-of-the-art
+performance across various ICL distribution shifts
+
+
+
+ comment: Ongoing work; preliminary version
+
+
+
+ + ☆ mPLUG-PaperOwl: Scientific Diagram Analysis with the Multimodal Large + Language Model +
+
+
+
+
+
+
+
+ Anwen Hu, Yaya Shi, Haiyang Xu, Jiabo Ye, Qinghao Ye, Ming Yan, Chenliang Li, Qi Qian, Ji Zhang, Fei Huang
+
+
+ Recently, the strong text creation ability of Large Language Models(LLMs) has
+given rise to many tools for assisting paper reading or even writing. However,
+the weak diagram analysis abilities of LLMs or Multimodal LLMs greatly limit
+their application scenarios, especially for scientific academic paper writing.
+In this work, towards a more versatile copilot for academic paper writing, we
+mainly focus on strengthening the multi-modal diagram analysis ability of
+Multimodal LLMs. By parsing Latex source files of high-quality papers, we
+carefully build a multi-modal diagram understanding dataset M-Paper. By
+aligning diagrams in the paper with related paragraphs, we construct
+professional diagram analysis samples for training and evaluation. M-Paper is
+the first dataset to support joint comprehension of multiple scientific
+diagrams, including figures and tables in the format of images or Latex codes.
+Besides, to better align the copilot with the user's intention, we introduce
+the `outline' as the control signal, which could be directly given by the user
+or revised based on auto-generated ones. Comprehensive experiments with a
+state-of-the-art Mumtimodal LLM demonstrate that training on our dataset shows
+stronger scientific diagram understanding performance, including diagram
+captioning, diagram analysis, and outline recommendation. The dataset, code,
+and model are available at
+https://github.com/X-PLUG/mPLUG-DocOwl/tree/main/PaperOwl.
+
+
+
+ comment: 20 pages, 12 figures
+
+
+
+ + ♻ ☆ Knowledge Pursuit Prompting for Zero-Shot Multimodal Synthesis +
+ +
+ Hallucinations and unfaithful synthesis due to inaccurate prompts with
+insufficient semantic details are widely observed in multimodal generative
+models. A prevalent strategy to align multiple modalities is to fine-tune the
+generator with a large number of annotated text-image pairs. However, such a
+procedure is labor-consuming and resource-draining. The key question we ask is:
+can we enhance the quality and faithfulness of text-driven generative models
+beyond extensive text-image pair annotations? To address this question, we
+propose Knowledge Pursuit Prompting (KPP), a zero-shot framework that
+iteratively incorporates external knowledge to help generators produce reliable
+visual content. Instead of training generators to handle generic prompts, KPP
+employs a recursive knowledge query process to gather informative external
+facts from the knowledge base, instructs a language model to compress the
+acquired knowledge for prompt refinement, and utilizes text-driven generators
+for visual synthesis. The entire process is zero-shot, without accessing the
+architectures and parameters of generative models. We evaluate the framework
+across multiple text-driven generative tasks (image, 3D rendering, and video)
+on datasets of different domains. We further demonstrate the extensibility and
+adaptability of KPP through varying foundation model bases and instructions.
+Our results show that KPP is capable of generating faithful and semantically
+rich content across diverse visual domains, offering a promising solution to
+improve multimodal generative models.
+
+
+
+
+ + ♻ ☆ A Baseline Analysis of Reward Models' Ability To Accurately Analyze + Foundation Models Under Distribution Shift +
+ +
+ Foundation models, specifically Large Language Models (LLM's), have lately
+gained wide-spread attention and adoption. Reinforcement Learning with Human
+Feedback (RLHF) involves training a reward model to capture desired behaviors,
+which is then used to align an LLM. These reward models are additionally used
+at inference-time to estimate how well LLM responses adhere to those desired
+behaviors. However, there is little work measuring how robust these reward
+models are to distribution shifts. In this work, we evaluate how reward model
+performance - measured via accuracy and calibration (i.e. alignment between
+accuracy and confidence) - is affected by distribution shift. We show novel
+calibration patterns and accuracy drops due to OOD prompts and responses, and
+that the reward model is more sensitive to shifts in responses than prompts.
+Additionally, we adapt an OOD detection technique commonly used in
+classification to the reward model setting in order to detect these
+distribution shifts in prompts and responses.
+
+
+
+
+ + ♻ ☆ Locally Differentially Private Document Generation Using Zero Shot + Prompting EMNLP 2023 +
+ +
+ Numerous studies have highlighted the privacy risks associated with
+pretrained large language models. In contrast, our research offers a unique
+perspective by demonstrating that pretrained large language models can
+effectively contribute to privacy preservation. We propose a locally
+differentially private mechanism called DP-Prompt, which leverages the power of
+pretrained large language models and zero-shot prompting to counter author
+de-anonymization attacks while minimizing the impact on downstream utility.
+When DP-Prompt is used with a powerful language model like ChatGPT (gpt-3.5),
+we observe a notable reduction in the success rate of de-anonymization attacks,
+showing that it surpasses existing approaches by a considerable margin despite
+its simpler design. For instance, in the case of the IMDB dataset, DP-Prompt
+(with ChatGPT) perfectly recovers the clean sentiment F1 score while achieving
+a 46\% reduction in author identification F1 score against static attackers and
+a 26\% reduction against adaptive attackers. We conduct extensive experiments
+across six open-source large language models, ranging up to 7 billion
+parameters, to analyze various effects of the privacy-utility tradeoff.
+
+
+
+ comment: Accepted at EMNLP 2023 (Findings)
+
+
+
+ + ♻ ☆ Focused Transformer: Contrastive Training for Context Scaling NeurIPS 2023 +
+
+
+
+
+
+
+
+ Szymon Tworkowski, Konrad Staniszewski, Mikołaj Pacek, Yuhuai Wu, Henryk Michalewski, Piotr Miłoś
+
+
+ Large language models have an exceptional capability to incorporate new
+information in a contextual manner. However, the full potential of such an
+approach is often restrained due to a limitation in the effective context
+length. One solution to this issue is to endow an attention layer with access
+to an external memory, which comprises of (key, value) pairs. Yet, as the
+number of documents increases, the proportion of relevant keys to irrelevant
+ones decreases, leading the model to focus more on the irrelevant keys. We
+identify a significant challenge, dubbed the distraction issue, where keys
+linked to different semantic values might overlap, making them hard to
+distinguish. To tackle this problem, we introduce the Focused Transformer
+(FoT), a technique that employs a training process inspired by contrastive
+learning. This novel approach enhances the structure of the (key, value) space,
+enabling an extension of the context length. Our method allows for fine-tuning
+pre-existing, large-scale models to lengthen their effective context. This is
+demonstrated by our fine-tuning of $3B$ and $7B$ OpenLLaMA checkpoints. The
+resulting models, which we name LongLLaMA, exhibit advancements in tasks
+requiring a long context. We further illustrate that our LongLLaMA models
+adeptly manage a $256 k$ context length for passkey retrieval.
+
+
+
+ comment: Accepted at 37th Conference on Neural Information Processing Systems
+ (NeurIPS 2023). 28 pages, 10 figures, 11 tables
+
+
+
+ + ♻ ☆ Supporting Human-AI Collaboration in Auditing LLMs with LLMs +
+ +
+ Large language models are becoming increasingly pervasive and ubiquitous in
+society via deployment in sociotechnical systems. Yet these language models, be
+it for classification or generation, have been shown to be biased and behave
+irresponsibly, causing harm to people at scale. It is crucial to audit these
+language models rigorously. Existing auditing tools leverage either or both
+humans and AI to find failures. In this work, we draw upon literature in
+human-AI collaboration and sensemaking, and conduct interviews with research
+experts in safe and fair AI, to build upon the auditing tool: AdaTest (Ribeiro
+and Lundberg, 2022), which is powered by a generative large language model
+(LLM). Through the design process we highlight the importance of sensemaking
+and human-AI communication to leverage complementary strengths of humans and
+generative models in collaborative auditing. To evaluate the effectiveness of
+the augmented tool, AdaTest++, we conduct user studies with participants
+auditing two commercial language models: OpenAI's GPT-3 and Azure's sentiment
+analysis model. Qualitative analysis shows that AdaTest++ effectively leverages
+human strengths such as schematization, hypothesis formation and testing.
+Further, with our tool, participants identified a variety of failures modes,
+covering 26 different topics over 2 tasks, that have been shown before in
+formal audits and also those previously under-reported.
+
+
+
+ comment: 21 pages, 3 figures
+
+
+
+ + ♻ ☆ ANPL: Towards Natural Programming with Interactive Decomposition +
+
+
+
+
+
+
+
+ Di Huang, Ziyuan Nan, Xing Hu, Pengwei Jin, Shaohui Peng, Yuanbo Wen, Rui Zhang, Zidong Du, Qi Guo, Yewen Pu, Yunji Chen
+
+
+ Though LLMs are capable of generating plausible programs, it's challenging to
+interact with the LLMs further to revise the program, especially if the user's
+specific requirements are different from the initial proposal. In this paper,
+we introduce ANPL, an interactive programming system that ensures users can
+always refine the generated code towards their specific programmatic intents
+via structured decompositions. Borrowing the paradigm of sketching from program
+synthesis, an ANPL program consists of a set of input-outputs that it must
+satisfy, a ``sketch'' -- control/data flow expressed in precise code (e.g.
+Python), and ``holes'' -- sub-modules to be implemented by the LLM specified
+with natural language. The user revises an ANPL program by either modifying the
+sketch, changing the language used to describe the holes, or providing
+additional input-outputs to a particular hole, turning it into a sub-ANPL
+program that can be solved recursively. This workflow allows the users to
+offload programming burdens to the LLM as much as possible while retaining the
+ability to pinpoint and resolve bugs locally, without exposing the rest of the
+program to the LLM. We deploy ANPL on the Abstraction and Reasoning Corpus
+(ARC), a set of unique tasks that are challenging for state-of-the-art AI
+systems, showing it outperforms baseline programming systems that (a) without
+the ability to decompose tasks interactively and (b) without the guarantee that
+the modules can be correctly composed together. Additional evaluations on APPS,
+HumanEval, and real-world programming tasks have validated that the ANPL
+framework is applicable to multiple programming domains. We release the ANPL
+solutions to the ARC tasks as a dataset, providing insights into how humans
+decompose novel tasks programmatically. See our code at
+https://iprc-dip.github.io/ANPL/.
+
+
+
+
+ + ♻ ☆ Mol-Instructions: A Large-Scale Biomolecular Instruction Dataset for + Large Language Models +
+
+
+
+
+
+
+
+ Yin Fang, Xiaozhuan Liang, Ningyu Zhang, Kangwei Liu, Rui Huang, Zhuo Chen, Xiaohui Fan, Huajun Chen
+
+
+ Large Language Models (LLMs), with their remarkable task-handling
+capabilities and innovative outputs, have catalyzed significant advancements
+across a spectrum of fields. However, their proficiency within specialized
+domains such as biomolecular studies remains limited. To address this
+challenge, we introduce Mol-Instructions, a comprehensive instruction dataset
+designed for the biomolecular domain. Mol-Instructions encompasses three key
+components: molecule-oriented instructions, protein-oriented instructions, and
+biomolecular text instructions. Each component aims to improve the
+understanding and prediction capabilities of LLMs concerning biomolecular
+features and behaviors. Through extensive instruction tuning experiments on
+LLMs, we demonstrate the effectiveness of Mol-Instructions in enhancing large
+models' performance in the intricate realm of biomolecular studies, thus
+fostering progress in the biomolecular research community. Mol-Instructions is
+publicly available for ongoing research and will undergo regular updates to
+enhance its applicability.
+
+
+
+ comment: Project homepage: https://github.com/zjunlp/Mol-Instructions, add
+ more experiments
+
+
+
+ + ♻ ☆ Controlling Pre-trained Language Models for Grade-Specific Text + Simplification EMNLP 2023 +
+ +
+ Text simplification (TS) systems rewrite text to make it more readable while
+preserving its content. However, what makes a text easy to read depends on the
+intended readers. Recent work has shown that pre-trained language models can
+simplify text using a wealth of techniques to control output simplicity,
+ranging from specifying only the desired reading grade level, to directly
+specifying low-level edit operations. Yet it remains unclear how to set these
+control parameters in practice. Existing approaches set them at the corpus
+level, disregarding the complexity of individual inputs and considering only
+one level of output complexity. In this work, we conduct an empirical study to
+understand how different control mechanisms impact the adequacy and simplicity
+of text simplification systems. Based on these insights, we introduce a simple
+method that predicts the edit operations required for simplifying a text for a
+specific grade level on an instance-per-instance basis. This approach improves
+the quality of the simplified outputs over corpus-level search-based
+heuristics.
+
+
+
+ comment: EMNLP 2023
+
+
+
+ + ♻ ☆ Generating More Pertinent Captions by Leveraging Semantics and Style on + Multi-Source Datasets +
+ +
+ This paper addresses the task of generating fluent descriptions by training
+on a non-uniform combination of data sources, containing both human-annotated
+and web-collected captions. Large-scale datasets with noisy image-text pairs,
+indeed, provide a sub-optimal source of supervision because of their
+low-quality descriptive style, while human-annotated datasets are cleaner but
+smaller in scale. To get the best of both worlds, we propose to leverage and
+separate semantics and descriptive style through the incorporation of a style
+token and keywords extracted through a retrieval component. The proposed model
+avoids the need of object detectors, is trained with a single objective of
+prompt language modeling, and can replicate the style of human-collected
+captions while training on sources with different input styles. Experimentally,
+the model shows a strong capability of recognizing real-world concepts and
+producing high-quality captions. Extensive experiments are performed on
+different image captioning datasets, including CC3M, nocaps, and the
+competitive COCO dataset, where our model consistently outperforms baselines
+and state-of-the-art approaches.
+
+
+
+ comment: Accepted to IJCV
+
+
+
+ + ♻ ☆ Analyzing Semantic Faithfulness of Language Models via Input + Intervention on Question Answering +
+ +
+ Transformer-based language models have been shown to be highly effective for
+several NLP tasks. In this paper, we consider three transformer models, BERT,
+RoBERTa, and XLNet, in both small and large versions, and investigate how
+faithful their representations are with respect to the semantic content of
+texts. We formalize a notion of semantic faithfulness, in which the semantic
+content of a text should causally figure in a model's inferences in question
+answering. We then test this notion by observing a model's behavior on
+answering questions about a story after performing two novel semantic
+interventions: deletion intervention and negation intervention. While
+transformer models achieve high performance on standard question answering
+tasks, we show that they fail to be semantically faithful once we perform these
+interventions for a significant number of cases (~50% for deletion
+intervention, and ~20% drop in accuracy for negation intervention). We then
+propose an intervention-based training regime that can mitigate the undesirable
+effects for deletion intervention by a significant margin (from ~ 50% to ~6%).
+We analyze the inner-workings of the models to better understand the
+effectiveness of intervention-based training for deletion intervention. But we
+show that this training does not attenuate other aspects of semantic
+unfaithfulness such as the models' inability to deal with negation intervention
+or to capture the predicate-argument structure of texts. We also test
+InstructGPT, via prompting, for its ability to handle the two interventions and
+to capture predicate-argument structure. While InstructGPT models do achieve
+very high performance on predicate-argument structure task, they fail to
+respond adequately to our deletion and negation interventions.
+
+
+
+
+ + ♻ ☆ Language Models as Black-Box Optimizers for Vision-Language Models +
+
+
+
+
+
+
+
+ Shihong Liu, Zhiqiu Lin, Samuel Yu, Ryan Lee, Tiffany Ling, Deepak Pathak, Deva Ramanan
+
+
+ Vision-language models (VLMs) pre-trained on web-scale datasets have
+demonstrated remarkable capabilities on downstream tasks when fine-tuned with
+minimal data. However, many VLMs rely on proprietary data and are not
+open-source, which restricts the use of white-box approaches for fine-tuning.
+As such, we aim to develop a black-box approach to optimize VLMs through
+natural language prompts, thereby avoiding the need to access model parameters,
+feature embeddings, or even output logits. We propose employing chat-based LLMs
+to search for the best text prompt for VLMs. Specifically, we adopt an
+automatic hill-climbing procedure that converges to an effective prompt by
+evaluating the performance of current prompts and asking LLMs to refine them
+based on textual feedback, all within a conversational process without
+human-in-the-loop. In a challenging 1-shot image classification setup, our
+simple approach surpasses the white-box continuous prompting method (CoOp) by
+an average of 1.5% across 11 datasets including ImageNet. Our approach also
+outperforms both human-engineered and LLM-generated prompts. We highlight the
+advantage of conversational feedback that incorporates both positive and
+negative prompts, suggesting that LLMs can utilize the implicit gradient
+direction in textual feedback for a more efficient search. In addition, we find
+that the text prompts generated through our strategy are not only more
+interpretable but also transfer well across different VLM architectures in a
+black-box manner. Lastly, we demonstrate our framework on a state-of-the-art
+black-box VLM (DALL-E 3) for text-to-image optimization.
+
+
+
+ comment: Project site: llm-can-optimize-vlm.github.io
+
+
+
+ + ♻ ☆ Temporal Information Extraction by Predicting Relative Time-lines EMNLP 2018 +
+ +
+ The current leading paradigm for temporal information extraction from text
+consists of three phases: (1) recognition of events and temporal expressions,
+(2) recognition of temporal relations among them, and (3) time-line
+construction from the temporal relations. In contrast to the first two phases,
+the last phase, time-line construction, received little attention and is the
+focus of this work. In this paper, we propose a new method to construct a
+linear time-line from a set of (extracted) temporal relations. But more
+importantly, we propose a novel paradigm in which we directly predict start and
+end-points for events from the text, constituting a time-line without going
+through the intermediate step of prediction of temporal relations as in earlier
+work. Within this paradigm, we propose two models that predict in linear
+complexity, and a new training loss using TimeML-style annotations, yielding
+promising results.
+
+
+
+ comment: Accepted at the Conference on Empirical Methods in Natural Language
+ Processing (EMNLP 2018). Small correction in Eq. 6 on 30 Nov. 2023
+
+
+
+ + ♻ ☆ On Context Utilization in Summarization with Large Language Models +
+ +
+ Large language models (LLMs) excel in zero-shot abstractive summarization
+tasks, delivering fluent and pertinent summaries. Recent advancements have
+extended their capabilities to handle long-input contexts, surpassing token
+limits of 100k. However, in the realm of multi-document question answering,
+language models exhibit uneven utilization of their input context. They tend to
+favor the initial and final segments, resulting in a U-shaped performance
+pattern concerning where the answer is located within the input. This bias
+raises concerns, particularly in summarization tasks where crucial content may
+be dispersed throughout the source document(s). This paper presents a
+comprehensive investigation encompassing 10 datasets, 5 LLMs, and 5 evaluation
+metrics to analyze how these models leverage their input for abstractive
+summarization. Our findings reveal a pronounced bias towards the introductory
+content (and to a lesser extent, the final content), posing challenges for LLM
+performance across a range of diverse summarization benchmarks.
+
+
+
+ comment: work in progress
+
+
+
+ + ♻ ☆ Predicting Emergent Abilities with Infinite Resolution Evaluation +
+
+
+
+
+
+
+
+ Shengding Hu, Xin Liu, Xu Han, Xinrong Zhang, Chaoqun He, Weilin Zhao, Yankai Lin, Ning Ding, Zebin Ou, Guoyang Zeng, Zhiyuan Liu, Maosong Sun
+
+
+ The scientific scale-up of large language models (LLMs) necessitates a
+comprehensive understanding of their scaling properties. However, the existing
+literature on the scaling properties only yields an incomplete answer:
+optimization loss decreases predictably as the model size increases, in line
+with established scaling law; yet no scaling law for task has been established
+and the task performances are far from predictable during scaling. Task
+performances typically show minor gains on small models until they improve
+dramatically once models exceed a size threshold, exemplifying the ``emergent
+abilities''. In this study, we discover that small models, although they
+exhibit minor performance, demonstrate critical and consistent task performance
+improvements that are not captured by conventional evaluation strategies due to
+insufficient measurement resolution. To measure such improvements, we introduce
+PassUntil, an evaluation strategy with theoretically infinite resolution,
+through massive sampling in the decoding phase. With PassUntil, we conduct a
+quantitative investigation into the scaling law of task performance. The
+investigation contains two parts. Firstly, a strict task scaling law that is
+not conventionally known to exist, is identified, enhancing the predictability
+of task performances. Remarkably, we are able to predict the performance of the
+2.4B model on code generation with merely 0.05\% deviation before training
+starts, which is the first systematic attempt to verify predictable scaling
+proposed by GPT-4's report. Secondly, we are able to study emergent abilities
+quantitatively. We identify a kind of accelerated emergence whose scaling curve
+cannot be fitted by standard scaling law function and has a increasing speed.
+We then examine two hypothesis and imply that the ``multiple circuits
+hypothesis'' might be responsible for the accelerated emergence.
+
+
+
+ comment: After revision
+
+
+
+ + ♻ ☆ Editing Large Language Models: Problems, Methods, and Opportunities EMNLP 2023 +
+
+
+
+
+
+
+
+ Yunzhi Yao, Peng Wang, Bozhong Tian, Siyuan Cheng, Zhoubo Li, Shumin Deng, Huajun Chen, Ningyu Zhang
+
+
+ Despite the ability to train capable LLMs, the methodology for maintaining
+their relevancy and rectifying errors remains elusive. To this end, the past
+few years have witnessed a surge in techniques for editing LLMs, the objective
+of which is to efficiently alter the behavior of LLMs within a specific domain
+without negatively impacting performance across other inputs. This paper
+embarks on a deep exploration of the problems, methods, and opportunities
+related to model editing for LLMs. In particular, we provide an exhaustive
+overview of the task definition and challenges associated with model editing,
+along with an in-depth empirical analysis of the most progressive methods
+currently at our disposal. We also build a new benchmark dataset to facilitate
+a more robust evaluation and pinpoint enduring issues intrinsic to existing
+techniques. Our objective is to provide valuable insights into the
+effectiveness and feasibility of each editing technique, thereby assisting the
+community in making informed decisions on the selection of the most appropriate
+method for a specific task or context. Code and datasets are available at
+https://github.com/zjunlp/EasyEdit.
+
+
+
+ comment: EMNLP 2023. Updated with new experiments
+
+
+
+ + ♻ ☆ Unified Segment-to-Segment Framework for Simultaneous Sequence + Generation NeurIPS 2023 +
+ +
+ Simultaneous sequence generation is a pivotal task for real-time scenarios,
+such as streaming speech recognition, simultaneous machine translation and
+simultaneous speech translation, where the target sequence is generated while
+receiving the source sequence. The crux of achieving high-quality generation
+with low latency lies in identifying the optimal moments for generating,
+accomplished by learning a mapping between the source and target sequences.
+However, existing methods often rely on task-specific heuristics for different
+sequence types, limiting the model's capacity to adaptively learn the
+source-target mapping and hindering the exploration of multi-task learning for
+various simultaneous tasks. In this paper, we propose a unified
+segment-to-segment framework (Seg2Seg) for simultaneous sequence generation,
+which learns the mapping in an adaptive and unified manner. During the process
+of simultaneous generation, the model alternates between waiting for a source
+segment and generating a target segment, making the segment serve as the
+natural bridge between the source and target. To accomplish this, Seg2Seg
+introduces a latent segment as the pivot between source to target and explores
+all potential source-target mappings via the proposed expectation training,
+thereby learning the optimal moments for generating. Experiments on multiple
+simultaneous generation tasks demonstrate that Seg2Seg achieves
+state-of-the-art performance and exhibits better generality across various
+tasks.
+
+
+
+ comment: Accepted at NeurIPS 2023
+
+
+
+ + ♻ ☆ Language Models as a Service: Overview of a New Paradigm and its + Challenges +
+
+
+
+
+
+
+
+ Emanuele La Malfa, Aleksandar Petrov, Simon Frieder, Christoph Weinhuber, Ryan Burnell, Raza Nazar, Anthony G. Cohn, Nigel Shadbolt, Michael Wooldridge
+
+
+ Some of the most powerful language models currently are proprietary systems,
+accessible only via (typically restrictive) web or software programming
+interfaces. This is the Language-Models-as-a-Service (LMaaS) paradigm. In
+contrast with scenarios where full model access is available, as in the case of
+open-source models, such closed-off language models present specific challenges
+for evaluating, benchmarking, and testing them. This paper has two goals: on
+the one hand, we delineate how the aforementioned challenges act as impediments
+to the accessibility, replicability, reliability, and trustworthiness of LMaaS.
+We systematically examine the issues that arise from a lack of information
+about language models for each of these four aspects. We conduct a detailed
+analysis of existing solutions and put forth a number of considered
+recommendations, and highlight the directions for future advancements. On the
+other hand, it serves as a comprehensive resource for existing knowledge on
+current, major LMaaS, offering a synthesized overview of the licences and
+capabilities their interfaces offer.
+
+
+
+
+ + ♻ ☆ Handwriting recognition and automatic scoring for descriptive answers in + Japanese language tests +
+ +
+ This paper presents an experiment of automatically scoring handwritten
+descriptive answers in the trial tests for the new Japanese university entrance
+examination, which were made for about 120,000 examinees in 2017 and 2018.
+There are about 400,000 answers with more than 20 million characters. Although
+all answers have been scored by human examiners, handwritten characters are not
+labeled. We present our attempt to adapt deep neural network-based handwriting
+recognizers trained on a labeled handwriting dataset into this unlabeled answer
+set. Our proposed method combines different training strategies, ensembles
+multiple recognizers, and uses a language model built from a large general
+corpus to avoid overfitting into specific data. In our experiment, the proposed
+method records character accuracy of over 97% using about 2,000 verified
+labeled answers that account for less than 0.5% of the dataset. Then, the
+recognized answers are fed into a pre-trained automatic scoring system based on
+the BERT model without correcting misrecognized characters and providing rubric
+annotations. The automatic scoring system achieves from 0.84 to 0.98 of
+Quadratic Weighted Kappa (QWK). As QWK is over 0.8, it represents an acceptable
+similarity of scoring between the automatic scoring system and the human
+examiners. These results are promising for further research on end-to-end
+automatic scoring of descriptive answers.
+
+
+
+ comment: Keywords: handwritten Japanese answers, handwriting recognition,
+ automatic scoring, ensemble recognition, deep neural networks; Reported in
+ IEICE technical report, PRMU2021-32, pp.45-50 (2021.12) Published after peer
+ review and Presented in ICFHR2022, Lecture Notes in Computer Science, vol.
+ 13639, pp. 274-284 (2022.11)
+
+
+
+ + ♻ ☆ Multi-turn Response Selection using Dialogue Dependency Relations EMNLP2020 +
+ +
+ Multi-turn response selection is a task designed for developing dialogue
+agents. The performance on this task has a remarkable improvement with
+pre-trained language models. However, these models simply concatenate the turns
+in dialogue history as the input and largely ignore the dependencies between
+the turns. In this paper, we propose a dialogue extraction algorithm to
+transform a dialogue history into threads based on their dependency relations.
+Each thread can be regarded as a self-contained sub-dialogue. We also propose
+Thread-Encoder model to encode threads and candidates into compact
+representations by pre-trained Transformers and finally get the matching score
+through an attention layer. The experiments show that dependency relations are
+helpful for dialogue context understanding, and our model outperforms the
+state-of-the-art baselines on both DSTC7 and DSTC8*, with competitive results
+on UbuntuV2.
+
+
+
+ comment: Accepted for publication as a long paper in EMNLP2020
+
+
+
+ + ♻ ☆ How to Build an AI Tutor that Can Adapt to Any Course and Provide + Accurate Answers Using Large Language Model and Retrieval-Augmented + Generation +
+ +
+ Artificial intelligence is transforming education through data-driven,
+personalized learning solutions. This paper introduces AI Tutor, an innovative
+web application that provides personalized tutoring in any subject using
+state-of-the-art Large Language Model (LLM). AI Tutor ingests course materials
+to construct an adaptive knowledge base tailored to the course. When students
+pose questions, it retrieves the most relevant information and generates
+detailed, conversational responses citing supporting evidence. The system is
+powered by advanced large language models and Retrieval-Augmented Generation
+(RAG) techniques for accurate, natural question answering. We present a
+fully-functional web interface and video demonstration that showcase AI Tutor's
+versatility across diverse subjects and its ability to produce pedagogically
+cogent responses. While an initial prototype, this work represents a pioneering
+step toward AI-enabled tutoring systems that can democratize access to
+high-quality, customized educational support.
+
+
+
+ comment: 9 pages, 5 figures
+
+
+
+ + ♻ ☆ CLOMO: Counterfactual Logical Modification with Large Language Models +
+
+
+
+
+
+
+
+ Yinya Huang, Ruixin Hong, Hongming Zhang, Wei Shao, Zhicheng Yang, Dong Yu, Changshui Zhang, Xiaodan Liang, Linqi Song
+
+
+ In this study, we delve into the realm of counterfactual reasoning
+capabilities of large language models (LLMs). Our primary objective is to
+cultivate the counterfactual thought processes within LLMs and rigorously
+assess these processes for their validity. Specifically, we introduce a novel
+task, Counterfactual Logical Modification (CLOMO), and a high-quality
+human-annotated benchmark. In this task, LLMs must adeptly alter a given
+argumentative text to uphold a predetermined logical relationship. To
+effectively evaluate a generation model's counterfactual capabilities, we
+propose an innovative evaluation metric, the LogicAware Counterfactual Score to
+directly evaluate the natural language output of LLMs instead of modeling the
+task as a multiple-choice problem. Analysis shows that the proposed automatic
+metric aligns well with human preference. Our experimental results show that
+while LLMs demonstrate a notable capacity for logical counterfactual thinking,
+there remains a discernible gap between their current abilities and human
+performance.
+
+
+
+
+ + ♻ ☆ SELF: Language-Driven Self-Evolution for Large Language Models +
+
+
+
+
+
+
+
+ Jianqiao Lu, Wanjun Zhong, Wenyong Huang, Yufei Wang, Fei Mi, Baojun Wang, Weichao Wang, Lifeng Shang, Qun Liu
+
+
+ Large Language Models (LLMs) have demonstrated remarkable versatility across
+various domains. To further advance LLMs, we propose 'SELF' (Self-Evolution
+with Language Feedback), a novel approach that enables LLMs to self-improve
+through self-reflection, akin to human learning processes. SELF initiates with
+a meta-skill learning process that equips the LLMs with capabilities for
+self-feedback and self-refinement. Subsequently, the model undergoes an
+iterative process of self-evolution. In each iteration, it utilizes an
+unlabeled dataset of instructions to generate initial responses. These
+responses are enhanced through self-feedback and self-refinement. The model is
+then fine-tuned using this enhanced data. The model undergoes progressive
+improvement through this iterative self-evolution process. Moreover, the SELF
+framework enables the model to apply self-refinement during inference, which
+further improves response quality. Our experiments in mathematics and general
+tasks demonstrate that SELF can enhance the capabilities of LLMs without human
+intervention. The SELF framework indicates a promising direction for the
+autonomous evolution of LLMs, transitioning them from passive information
+receivers to active participants in their development.
+
+
+
+ comment: 17 pages, 4 figures, 11 tables
+
+
+
+ + ♻ ☆ Improving the Robustness of Transformer-based Large Language Models with + Dynamic Attention +
+
+
+
+
+
+
+
+ Lujia Shen, Yuwen Pu, Shouling Ji, Changjiang Li, Xuhong Zhang, Chunpeng Ge, Ting Wang
+
+
+ Transformer-based models, such as BERT and GPT, have been widely adopted in
+natural language processing (NLP) due to their exceptional performance.
+However, recent studies show their vulnerability to textual adversarial attacks
+where the model's output can be misled by intentionally manipulating the text
+inputs. Despite various methods that have been proposed to enhance the model's
+robustness and mitigate this vulnerability, many require heavy consumption
+resources (e.g., adversarial training) or only provide limited protection
+(e.g., defensive dropout). In this paper, we propose a novel method called
+dynamic attention, tailored for the transformer architecture, to enhance the
+inherent robustness of the model itself against various adversarial attacks.
+Our method requires no downstream task knowledge and does not incur additional
+costs. The proposed dynamic attention consists of two modules: (I) attention
+rectification, which masks or weakens the attention value of the chosen tokens,
+and (ii) dynamic modeling, which dynamically builds the set of candidate
+tokens. Extensive experiments demonstrate that dynamic attention significantly
+mitigates the impact of adversarial attacks, improving up to 33\% better
+performance than previous methods against widely-used adversarial attacks. The
+model-level design of dynamic attention enables it to be easily combined with
+other defense methods (e.g., adversarial training) to further enhance the
+model's robustness. Furthermore, we demonstrate that dynamic attention
+preserves the state-of-the-art robustness space of the original model compared
+to other dynamic modeling methods.
+
+
+
+
+ + ♻ ☆ Do pretrained Transformers Really Learn In-context by Gradient Descent? +
+ +
+ The emergence of In-Context Learning (ICL) in LLMs remains a significant
+phenomenon with little understanding. To explain ICL, recent studies try to
+shed light on ICL by connecting it to Gradient Descent (GD). However, the
+question is, do these hold up in practice in actual pre-trained models?
+ We highlight the limiting assumptions in prior works that make their context
+considerably different from the practical context in which language models are
+trained. For example, the theoretical hand-constructed weights used in these
+studies have properties that don't match those of real LLMs. Furthermore, their
+experimental verification uses \emph{ICL objective} (training models explicitly
+for ICL), which differs from the emergent ICL in the wild.
+ We also look for evidence in real models. We observe that ICL and GD have
+different sensitivity to the order in which they observe demonstrations.
+Finally, we probe and compare the ICL vs. GD hypothesis in a natural setting.
+We conduct comprehensive empirical analyses on language models pre-trained on
+natural data (LLaMa-7B). Our comparisons of three performance metrics highlight
+the inconsistent behavior of ICL and GD as a function of various factors such
+as datasets, models, and the number of demonstrations. We observe that ICL and
+GD modify the output distribution of language models differently. These results
+indicate that the equivalence between ICL and GD remains an open hypothesis and
+calls for further studies.
+
+
+
+
+ + ♻ ☆ Probing Quantifier Comprehension in Large Language Models: Another + Example of Inverse Scaling EMNLP 2023 +
+ +
+ With their increasing size, large language models (LLMs) are becoming
+increasingly good at language understanding tasks. But even with high
+performance on specific downstream task, LLMs fail at simple linguistic tests
+for negation or quantifier understanding. Previous work on quantifier
+understanding in LLMs show inverse scaling in understanding few-type
+quantifiers. In this paper, we question the claims of of previous work and show
+that it is a result of inappropriate testing methodology. We also present
+alternate methods to measure quantifier comprehension in LLMs and show that
+LLMs are able to better understand the difference between the meaning of
+few-type and most-type quantifiers as their size increases, although they are
+not particularly good at it. We also observe inverse scaling for most-type
+quantifier understanding, which is contrary to human psycho-linguistic
+experiments and previous work, where the model's understanding of most-type
+quantifier gets worse as the model size increases. We do this evaluation on
+models ranging from 125M-175B parameters, which suggests that LLMs do not do as
+well as expected with quantifiers. We also discuss the possible reasons for
+this and the relevance of quantifier understanding in evaluating language
+understanding in LLMs.
+
+
+
+ comment: Accepted to BlackboxNLP (EMNLP 2023)
+
+