Official Repo for paper MINT: Evaluating LLMs in Multi-turn Interaction with Tools and Language Feedback by Xingyao Wang*, Zihan Wang*, Jiateng Liu, Yangyi Chen, Lifan Yuan, Hao Peng and Heng Ji.

MINT benchmark aims to evaluate LLMs' ability to solve tasks with multi-turn interactions by (1) using tools and (2) leveraging natural language feedback.

🏆 Please visit our website for the leaderboard.

⚠️ WARNING: Evaluation of LLMs requires executing untrusted model-generated code. Users are strongly encouraged to sandbox the code execution so that it does not perform destructive actions on their host or network. We highly recommend using the provided docker image for isolated execution.

git clone https://github.com/xingyaoww/mint-bench
cd mint-bench

You can choose to use docker (recommended) or local setup as follows.

You only need to ensure that you have docker installed on your local computer following the official guide.

# Install Anaconda or Miniconda first if you don't have it already.
conda env create -f environment.yml
conda activate mint
# Install the MINT package
pip install -e .

To evaluate huggingface-compatible open-source models, check instructions here.

To evaluate API-base closed-source models, set Your API Key:

# Obtain OpenAI API access from https://openai.com/blog/openai-api
# This key is necessary for all models since, by default, we use GPT-4 for feedback generation
export OPENAI_API_KEY='YOUR KEY HERE';

# The following keys are optional
# Will only be used if you use Bard or Claude as the evaluated model/feedback provider
# https://www.googlecloudcommunity.com/gc/AI-ML/Google-Bard-API/m-p/538517
export BARD_API_KEY='YOUR KEY HERE';
# https://docs.anthropic.com/claude/docs/getting-access-to-claude
export ANTHROPIC_API_KEY='YOUR KEY HERE';

Please refer to docs/CONFIG.md if you want to evaluate a custom API-based model.

MINT uses configuration files to specify the experiment settings (e.g., which models to use, which datasets to use, etc.). We provide a script mint/configs/config_variables.py to generate default config files for you.

Please refer to docs/CONFIG.md if you want to add an evaluated model, a different LLM that provides feedback, or different experiment setting (e.g., feedback type).

# Create configuration files for your experiment.
# By default configs will be saved to configs/ and model outputs will be saved to data/outputs
python mint/configs/generate_config.py

If you want to use a docker environment for better isolation: We recommend using a terminal multiplexer (e.g., tmux) to start the docker for better management. Please make sure you have admin permission by adding the prefix "sudo" or have your admin add you to the docker user group. You will enter an interactive docker environment with all dependencies installed:

./scripts/docker/run_interactive.sh

Run the following once you are inside the docker container (for docker-setup), or you are in the conda environment (conda activate mint):

# Use this script to run the experiment
# You can specify which config file you want to run by modifying the script
# By default, we will run multiple configs in parallel and save output to `output.txt` in corresponding outputs folder
./scripts/run.sh

# If you want to run the experiment in debug mode (only run 1 config at a time), run the following
DEBUG_MODE=1 ./scripts/run.sh

Analyze result: You can use the notebook scripts/notebook/analyze_output.ipynb to analyze the model you evaluated and reproduce tables and figures from the paper. Alternatively, you can convert output by running python scripts/convert_outputs.py --data_dir data/outputs --output_dir YOUR_OUTPUT_DIR/ to get results breakdown by task name.

Visualize the results: The following code will starts a streamlit-based visualizer to visualize your directory.

streamlit run scripts/visualizer.py -- --data_dir data/outputs

We welcome contributions to this repo from the community!

To add your model to the benchmark, you can start a PR to merge your model outputs data/outputs into this repo. This will be automatically updated to the website. Please refer to docs/CONTRIBUTING.md for more details about contributing models, tools, and data.

If you find this repo helpful, please cite our paper:

@misc{wang2023mint,
      title={MINT: Evaluating LLMs in Multi-turn Interaction with Tools and Language Feedback},
      author={Xingyao Wang and Zihan Wang and Jiateng Liu and Yangyi Chen and Lifan Yuan and Hao Peng and Heng Ji},
      year={2023},
      eprint={2309.10691},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}