An Interactive Evaluation Framework for Human-Language Model Interaction
This repository contains the code for HALIE (Human-AI Language-based Interaction Evaluation), a new framework for evaluating human-LM interaction. It is designed to be flexible and extensible to support a variety of interactive tasks beyond the five tasks we considered in our paper: social dialogue, question answering, crossword puzzles, text summarization, and metaphor generation. All the code for the five tasks are included in this repository.
- Paper: Evaluating Human-Language Model Interaction (Mina Lee, Megha Srivastava, Amelia Hardy, John Thickstun, Esin Durmus, Ashwin Paranjape, Ines Gerard-Ursin, Xiang Lisa Li, Faisal Ladhak, Frieda Rong, Rose E. Wang, Minae Kwon, Joon Sung Park, Hancheng Cao, Tony Lee, Rishi Bommasani, Michael Bernstein, Percy Liang, 2022)
- Blog: Observations from HALIE: A Closer Look at Human-LM Interactions in Information-Seeking Contexts (Megha Srivastava, John Thickstun, 2023)
If you have any questions, please reach out to Mina Lee at [email protected]. For scenario-specific questions or feedback (e.g. task design, data issues), please include the following authors:
- Social Dialogue: Amelia Hardy (
[email protected]), Ashwin Paranjape ([email protected]), Ines Gerard-Ursin ([email protected]), Esin Durmus ([email protected]), Faisal Ladhak ([email protected]), Joon Sung Park ([email protected]) - Question Answering: Megha Srivastava (
[email protected]), John Thickstun ([email protected]), Rose Wang ([email protected]), Minae Kwon ([email protected]) - Crossword Puzzles: Megha Srivastava (
[email protected]) - Text Summarization: Esin Durmus (
[email protected]), Faisal Ladhak ([email protected]) - Metaphor Generation: Frieda Rong (
[email protected]), Xiang Lisa Li ([email protected])
See README for a more comprehensieve overview of data in HALIE.
Standardized data. If you simply want to take a look at our data or use it to perform your own analysis, you can find the standardized data for the five tasks in ./data/std.
Raw data. On the other hand, if you want to standardize data from raw data we collected (./data/raw), you can follow the steps below to convert the raw data.
First, install the required packages:
pip3 install -r requirements.txt
Then, run the following command to standardize the logs (e.g., for question answering):
python3 ./src/run_question.py
The above command reads the raw data at ./data/raw/question and saves the standardized data at ./data/std/question. For the other four tasks, replace question with the name of the task you want to standardize in the path as well as command.
Visualizations. Static and dynamic visualizations of our data, which allows for an easier way for looking at raw interaction traces, is included at ./data/visualizations.
Your data. If you are interested in extending HALIE to support a new task, please create a PR or contact Mina Lee at [email protected].
We provide Jupyter Notebook files for analyzing the data collected for HALIE. The files are stored in ./notebook.
We currently provide code for the interfaces used to collect interaction traces for both the crossword puzzles and question answering scenarios in HALIE, located in ./interfaces.
We unfortunately do not currently release code for the text summarization, metaphor generation, and social dialogue scenarios. Those scenarios were built using code from CoAuthor and Chirpy Cardinal, and please feel free to create a PR for any information.