Code for the Paper "MathVista: Evaluating Mathematical Reasoning of Foundation Models in Visual Contexts".
For more details, please refer to the project page with dataset exploration and visualization tools: https://mathvista.github.io/.
🔔 If you have any questions or suggestions, please don't hesitate to let us know. You can comment on the Twitter, or post an issue on this repository.
[Webpage] [Paper] [Huggingface Dataset] [Leaderboard] [Visualization] [Result Explorer] [Twitter]
Tentative logo for MathVista.
- [2023.10.16] 🔍 We are working on a comparative study on the GPT-4V model. Stay tuned for the detailed report! 📑.
- [2023.10.15] We finished the manual evaluation of GPT-4V with the playground chatbot on the testmini set on MathVista. 🚀 GPT-4V achieves a substantial gain of 15.1% ⬆️ over Bard, reaching a new record of 49.9%! 🎉
- [2023.10.15] Our dataset is now accessible at Huggingface Datasets.
- [2023.10.15] Our dataset is now accessible at Paper With Code.
- [2023.10.03] The top-performing model, 🎭 Multimodal Bard, achieved a score of 34.8% on the testmini set for MathVista 📊.
- [2023.10.03] Our work was featured by Aran Komatsuzaki on Twitter. Thanks!
- [2023.10.03] Our paper is now accessible at https://arxiv.org/abs/2310.02255.
We present MathVista, a benchmark designed to amalgamate challenges from diverse mathematical and visual tasks. We first taxonomize the key task types, reasoning skills, and visual contexts from the literature to guide our selection from 28 existing math-focused and visual question answering datasets. Then, we construct three new datasets, IQTest, FunctionQA, and PaperQA, to accommodate for missing types of visual contexts. The problems featured often require deep visual understanding beyond OCR or image captioning, and compositional reasoning with rich domain-specific tools, thus posing a notable challenge to existing models.
Source dataset distribution of MathVista.
We conduct a comprehensive evaluation of 12 prominent open-source and proprietary foundation models (LLMs, LLMs augmented with tools, and LMMs. The top-performing model, Multimodal Bard, achieves only 58% of human performance (34.8% vs 60.3%), indicating ample room for further improvement. The performance of GPT-4V is astounding: it achieves a 15.1% improvement over Bard! But it still lags behind humans by 10.4%.
Accuracy scores the testmini set (1,000 examples) of MathVista.
Accuracy scores of one leading LLM (i.e., PoT GPT-4), four primary LMMs, random chance, and human performance on MathVista.
For more details, you can find our project page here and our paper here.
🚨🚨 The leaderboard is continuously being updated. To submit your results to the leaderboard, please send to this mail with your result json file (the result template for testmini and the template for test).
Accuracy scores on the testmini subset (1,000 examples):
| # | Model | Method | Source | Date | ALL | FQA | GPS | MWP | TQA | VQA | ALG | ARI | GEO | LOG | NUM | SCI | STA |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| - | Human | - | Link | 2023-10-03 | 60.3 | 59.7 | 48.4 | 73.0 | 63.2 | 55.9 | 50.9 | 59.2 | 51.4 | 40.7 | 53.8 | 64.9 | 63.9 |
| 1 | GPT-4V (Playgroud)🥇 | LMM 🖼️ | Link | 2023-10-15 | 49.9 | 43.1 | 50.5 | 57.5 | 65.2 | 38.0 | 53.0 | 49.0 | 51.0 | 21.6 | 20.1 | 63.1 | 55.8 |
| 2 | Multimodal Bard 🥈 | LMM 🖼️ | Link | 2023-10-03 | 34.8 | 26.0 | 47.1 | 29.6 | 48.7 | 26.8 | 46.5 | 28.6 | 47.8 | 13.5 | 14.9 | 47.5 | 33.0 |
| 3 | PoT GPT-4 (Caption+OCR) 🥉 | Tool 🛠️ | Link | 2023-10-03 | 33.9 | 30.1 | 39.4 | 30.6 | 39.9 | 31.3 | 37.4 | 31.7 | 41.0 | 18.9 | 20.1 | 44.3 | 37.9 |
| 4 | CoT GPT-4 (Caption+OCR) | Tool 🛠️ | Link | 2023-10-03 | 33.2 | 27.9 | 31.7 | 31.2 | 51.9 | 28.5 | 33.5 | 30.9 | 32.2 | 13.5 | 12.5 | 58.2 | 37.9 |
| 5 | CoT ChatGPT (Caption+OCR) | Tool 🛠️ | Link | 2023-10-03 | 33.2 | 27.5 | 29.3 | 36.0 | 49.4 | 29.1 | 31.0 | 32.9 | 31.0 | 16.2 | 17.4 | 50.8 | 37.2 |
| 6 | CoT Claude-2 (Caption+OCR) | Tool 🛠️ | Link | 2023-10-03 | 33.2 | 26.0 | 31.7 | 35.5 | 48.1 | 30.2 | 32.4 | 32.3 | 33.0 | 16.2 | 17.4 | 54.9 | 36.2 |
| 7 | PoT ChatGPT (Caption+OCR) | Tool 🛠️ | Link | 2023-10-03 | 26.8 | 24.5 | 26.4 | 23.7 | 33.5 | 27.9 | 27.8 | 26.1 | 28.0 | 18.9 | 13.2 | 33.6 | 29.9 |
| 8 | LLaVA (LLaMA-2-13B) | LMM 🖼️ | Link | 2023-10-03 | 26.1 | 26.8 | 29.3 | 16.1 | 32.3 | 26.3 | 27.3 | 20.1 | 28.8 | 24.3 | 18.3 | 37.3 | 25.1 |
| 9 | InstructBLIP (Vicuna-7B) | LMM 🖼️ | Link | 2023-10-03 | 25.3 | 23.1 | 20.7 | 18.3 | 32.3 | 35.2 | 21.8 | 27.1 | 20.7 | 18.9 | 20.4 | 33.0 | 23.1 |
| 10 | LLaVAR | LMM 🖼️ | Link | 2023-10-03 | 25.2 | 21.9 | 25.0 | 16.7 | 34.8 | 30.7 | 24.2 | 22.1 | 23.0 | 13.5 | 15.3 | 42.6 | 21.9 |
| 11 | LLaMA-Adapter-V2 (7B) | LMM 🖼️ | Link | 2023-10-03 | 23.9 | 21.2 | 25.5 | 11.3 | 32.3 | 31.8 | 26.3 | 20.4 | 24.3 | 24.3 | 13.9 | 29.5 | 18.3 |
| 12 | miniGPT4 (LLaMA-2-7B) | LMM 🖼️ | Link | 2023-10-03 | 23.1 | 18.6 | 26.0 | 13.4 | 30.4 | 30.2 | 28.1 | 21.0 | 24.7 | 16.2 | 16.7 | 25.4 | 17.9 |
| 13 | mPLUG-Owl (LLaMA-7B) | LMM 🖼️ | Link | 2023-10-03 | 22.2 | 22.7 | 23.6 | 10.2 | 27.2 | 27.9 | 23.6 | 19.2 | 23.9 | 13.5 | 12.7 | 26.3 | 21.4 |
| 14 | IDEFICS (9B-Instruct) | LMM 🖼️ | Link | 2023-10-03 | 19.8 | 21.6 | 21.1 | 6.5 | 25.9 | 24.0 | 22.1 | 15.0 | 19.8 | 18.9 | 9.9 | 24.6 | 18.1 |
| 15 | Random Chance | - | Link | 2023-10-03 | 17.9 | 18.2 | 21.6 | 3.8 | 19.6 | 26.3 | 21.7 | 14.7 | 20.1 | 13.5 | 8.3 | 17.2 | 16.3 |
Some notations in the table:
-
GPT-4V (Playgroud): the launched playground at https://chat.openai.com/?model=gpt-4; experimental dates range from Oct 7, 2023, to Oct 15, 2023
-
GPT-4: the
gpt-4-0613engine -
Method types
- LMM 🖼️: Large Multimodal Model
- Tool 🛠️: Tool-augmented Large Language Model
-
Task types:
- FQA: figure question answering
- GPS: geometry problem solving
- MWP: math word problem solving
- TQA: textbook question answering
- VQA: visual question answering
-
Mathematical reasoning types:
- ALG: algebraic reasoning
- ARI: arithmetic reasoning
- GEO: geometry reasoning
- LOG: logical reasoning
- NUM: numeric commonsense reasoning
- SCI: scientific reasoning
- STA: statistical reasoning
🔔 The leaderboard for the test set (5,141 examples) and the automatic evaluation on CodaLab are under construction.
Examples of our newly annotated datasets: IQTest, FunctionQA, and PaperQA:
🔍 Click to expand/collapse more examples
Examples of seven mathematical reasoning skills:
- Arithmetic Reasoning
- Statistical Reasoning
- Algebraic Reasoning
- Geometry Reasoning
- Numeric Commonsense Reasoning
- Scientific Reasoning
- Logical Reasoning
The MathVista dataset is derived from three newly collected datasets: IQTest, FunctionQA, and Paper, as well as 28 other source datasets. Details can be found in the source.json file. All these source datasets have been preprocessed and labeled for evaluation purposes.
All the data examples were divided into two subsets: testmini and test.
- testmini: 1,000 examples used for model development, validation, or for those with limited computing resources.
- test: 5,141 examples for standard evaluation. Notably, the answer labels for test will NOT be publicly released.
You can download this dataset by the following command (make sure that you have installed Huggingface Datasets):
from datasets import load_dataset
dataset = load_dataset("AI4Math/MathVista")Here are some examples of how to access the downloaded dataset:
# print the first example on the testmini set
print(dataset["testmini"][0])
print(dataset["testmini"][0]['pid']) # print the problem id
print(dataset["testmini"][0]['question']) # print the question text
print(dataset["testmini"][0]['query']) # print the query text
print(dataset["testmini"][0]['image']) # print the image path
print(dataset["testmini"][0]['answer']) # print the answer
dataset["testmini"][0]['decoded_image'] # display the image
# print the first example on the test set
print(dataset["test"][0])The dataset is provided in json format and contains the following attributes:
{
"question": [string] The question text,
"image": [string] A file path pointing to the associated image,
"choices": [list] Choice options for multiple-choice problems. For free-form problems, this could be a 'none' value,
"unit": [string] The unit associated with the answer, e.g., "m^2", "years". If no unit is relevant, it can be a 'none' value,
"precision": [integer] The number of decimal places the answer should be rounded to,
"answer": [string] The correct answer for the problem,
"question_type": [string] The type of question: "multi_choice" or "free_form",
"answer_type": [string] The format of the answer: "text", "integer", "float", or "list",
"pid": [string] Problem ID, e.g., "1",
"metadata": {
"split": [string] Data split: "testmini" or "test",
"language": [string] Question language: "English", "Chinese", or "Persian",
"img_width": [integer] The width of the associated image in pixels,
"img_height": [integer] The height of the associated image in pixels,
"source": [string] The source dataset from which the problem was taken,
"category": [string] The category of the problem: "math-targeted-vqa" or "general-vqa",
"task": [string] The task of the problem, e.g., "geometry problem solving",
"context": [string] The visual context type of the associated image,
"grade": [string] The grade level of the problem, e.g., "high school",
"skills": [list] A list of mathematical reasoning skills that the problem tests
},
"query": [string] the query text used as input (prompt) for the evaluation model
}
🎰 You can explore the dataset in an interactive way here.
Click to expand/collapse the visualization page screeshot.
We offer a few demo examples for using the dataset, as follows:
- Use the Bard API for inference: bard_local_demo.ipynb
Stay tuned for more demos coming soon!
Install the Python dependencies if you would like to reproduce our results for ChatGPT, GPT-4, Claude-2, and Bard:
pip install openai # for ChatGPT and GPT-4
pip install anthropic # for Claude-2
pip install bardapi # for BardFor more details, please refer to:
If you are considering evaluating your own model, these dependencies might be optional.
We provide images in the JPG format. You can download and unzip them using the following commands:
cd data
wget https://huggingface.co/datasets/AI4Math/MathVista/resolve/main/images.zip
unzip & rm images.zipThis step might be optional if you prefer to use the Hugging Face format of the data.
Recent foundation models have been trained to generate longer responses instead of brief text. As such, we propose a new strategy for benchmarking MathVista. This evaluation process comprises three stages:
(Step 1) Response Generation (generate_response.py): The models generate responses based on the given input query (prompt). This input query integrates the task description, the question, choices, and metadata. Such a design encourage the models yield responses in the desired format, subsequently enhancing the overall evaluation scores. An example of such an input query is:
Hint: Please answer the question and provide the correct option letter, e.g., A, B, C, D, at the end.
Question: Find $m\\angle H$
Choices:
(A) 97
(B) 102
(C) 107
(D) 122
The task description is defined as follows:
| Question type | Answer type | Task instruction |
|---|---|---|
| Multiple-choice | Text | Please answer the question and provide the correct option letter, e.g., A, B, C, D, at the end. |
| Free-form | Integer | Please answer the question requiring an integer answer and provide the final value, e.g., 1, 2, 3, at the end. |
| Free-form | Float (1) | Please answer the question requiring a floating-point number with one decimal place and provide the final value, e.g., 1.2, 1.3, 1.4, at the end. |
| Free-form | Float (2) | Please answer the question requiring a floating-point number with two decimal places and provide the final value, e.g., 1.23, 1.34, 1.45, at the end. |
| Free-form | List | Please answer the question requiring a Python list as an answer and provide the final list, e.g., [1, 2, 3], [1.2, 1.3, 1.4], at the end. |
(Step 2) Answer Extraction (extract_answer.py): Next, the short answer text is extracted from the detailed response. We propose an answer extractor based on LLMs such as GPT-4. A preliminary study of 200 examples shows that GPT-4 can extract the answer text with more than 99.5% accuracy. Below are examples of extracting short answers from long responses:
# Example 1
Hint: Please answer the question requiring an integer answer and provide the final value,
e.g., 1, 2, 3, at the end.
Question: Which number is missing?
Model response: The number missing in the sequence is 14.
Extracted answer: 14
# Example 2
Hint: Please answer the question and provide the correct option letter, e.g., A, B, C,
D, at the end.
Question: What fraction of the shape is blue?
Choices:
(A) 3/11
(B) 8/11
(C) 6/11
(D) 3/5
Model response: The correct answer is (B) 8/11.
Extracted answer: B
(Step 3) Score Calculation (calculate_score.py): Finally, the extracted answer is normalized to a required answer format (e.g., an option letter or an integer), and the target metric scores are computed.
To execute the evaluation scripts in our paper, ensure your data folder has the following structure:
├── query.json
├── test.json
├── testmini.json
├── images
├── 1.jpg
├── 2.jpg
└── ...
└── texts
├── captions_bard.json
└── ocrs_easyocr.json
Additionally, ensure that the API keys for ChatGPT, GPT-4, Claude-2, and Bard are properly set up.
If you have setted Multimodal Bard, you can run the following commands:
Generate the response:
cd evaluation
python generate_response.py \
--model bard \
--output_dir ../results/bard \
--output_file output_bard.jsonExtract the short answer text for score calculation:
python extract_answer.py \
--output_dir ../results/bard \
--output_file output_bard.json Calculate the final score:
python calculate_score.py \
--output_dir ../results/bard \
--output_file output_bard.json \
--score_file scores_bard.jsonGenerate the response:
cd evaluation
python generate_response.py \
--model gpt-4-0613 \
--output_dir ../results/gpt4 \
--output_file output_gpt4_2shot_solution_use_caption_ocr.json \
--shot_num 2 \
--shot_type solution \
--use_caption \
--use_ocr \
--caption_file ../data/texts/captions_bard.json \
--ocr_file ../data/texts/ocrs_easyocr.json Extract the short answer text for score calculation:
python extract_answer.py \
--output_dir ../results/gpt4 \
--output_file output_gpt4_2shot_solution_use_caption_ocr.jsonCalculate the final score:
python calculate_score.py \
--output_dir ../results/gpt4 \
--output_file output_gpt4_2shot_solution_use_caption_ocr.json \
--score_file scores_gpt4_2shot_solution_use_caption_ocr.jsonGenerate the response:
cd evaluation
python generate_response.py \
--model gpt-4-0613 \
--output_dir ../results/gpt4 \
--output_file output_gpt4_2shot_code_use_caption_ocr.json \
--shot_num 2 \
--shot_type code \
--use_caption \
--use_ocr \
--caption_file ../data/texts/captions_bard.json \
--ocr_file ../data/texts/ocrs_easyocr.json Extract the short answer text for score calculation:
python extract_answer.py \
--output_dir ../results/gpt4 \
--output_file output_gpt4_2shot_code_use_caption_ocr.json \
--response_label executionCalculate the final score:
python calculate_score.py \
--output_dir ../results/gpt4 \
--output_file output_gpt4_2shot_code_use_caption_ocr.json \
--score_file scores_gpt4_2shot_code_use_caption_ocr.jsonFor additional settings for large language models and other baselines, please refer to the running scripts available in the scripts directory.
We thank Hritik Bansal and the VisIT-Bench project for providing easy-to-use codes for evaluating most of the large multimodal models included in our paper.
Click to expand/collapse the examples.
Click to expand/collapse the examples.
Click to expand/collapse the example.
Click to expand/collapse the example.
Click to expand/collapse the example.
Click to expand/collapse the example.
🐙 For visualization of these results, visit our exploration page.
The new contributions to our dataset are distributed under the CC BY-SA 4.0 license, including
- The creation of three dataset: IQTest, FunctionQA, and Paper;
- The filtering and cleaning of source datasets;
- The standard formalization of instances for evaluation purposes;
- The annotations of metadata.
The copyright of the images and the questions belongs to the original authors, and the source of every image and original question can be found in the metadata field and in the source.json file. Alongside this license, the following conditions apply:
- Purpose: The dataset was primarily designed for use as a test set.
- Commercial Use: The dataset can be used commercially as a test set, but using it as a training set is prohibited. By accessing or using this dataset, you acknowledge and agree to abide by these terms in conjunction with the CC BY-SA 4.0 license.
Fantastic! I'm always open to engaging discussions, collaborations, or even just sharing a virtual coffee. To get in touch, visit Pan Lu's homepage for contact information.
If you find MathVista useful for your your research and applications, please kindly cite using this BibTeX:
@article{lu2023mathvista,
title={MathVista: Evaluating Mathematical Reasoning of Foundation Models in Visual Contexts},
author={Lu, Pan and Bansal, Hritik and Xia, Tony and Liu, Jiacheng and Li, Chunyuan and Hajishirzi, Hannaneh and Cheng, Hao and Chang, Kai-Wei and Galley, Michel and Gao, Jianfeng},
journal={arXiv preprint arXiv:2310.02255},
year={2023}
}Explore our additional research on large language models and large multimodal models , focusing on mathematical reasoning, scientific reasoning, and multimodal reasoning:
- [Chameleon] Chameleon: Plug-and-Play Compositional Reasoning with Large Language Models
- [ScienceQA] Learn to Explain: Multimodal Reasoning via Thought Chains for Science Question Answering
- [LLaMA-Adapter] LLaMA-Adapter: Efficient Fine-tuning of Language Models with Zero-init Attention
- [LLaMA-Adapter V2] LLaMA-Adapter V2: Parameter-Efficient Visual Instruction Model
- [DL4MATH] A Survey of Deep Learning for Mathematical Reasoning
- [PromptPG] Dynamic Prompt Learning via Policy Gradient for Semi-structured Mathematical Reasoning
- [SciBench] SciBench: Evaluating College-Level Scientific Problem-Solving Abilities of Large Language Models
- [TheoremQA] TheoremQA: A Theorem-driven Question Answering dataset
- [Līla] A Unified Benchmark for Mathematical Reasoning
- [IconQA] IconQA: A New Benchmark for Abstract Diagram Understanding and Visual Language Reasoning
- [Inter-GPS] Inter-GPS: Interpretable Geometry Problem Solving with Formal Language and Symbolic Reasoning
Here are the key contributors to this project:
Pan Lu1, Hritik Bansal1, Tony Xia1, Jiacheng Liu2, Chunyuan Li3, Hannaneh Hajishirzi2, Hao Cheng3, Kai-Wei Chang1, Michel Galley3, Jianfeng Gao3
1University of California, Los Angeles, 2University of Washington, 3Microsoft Research