Mini Judge

Simple implementation of LLM-As-Judge for pairwise evaluation of Q&A models.

Installation

Install the package using pip: pip install mini-judge

Usage

First, set the OPENAI_API_KEY environment variable to your OpenAI API key. Then, you can run the following command to evaluate the candidate answers in candidate_answers_path against the reference answers in ref_answers_path using judge_model as the judge model.

mini-judge \
--judge_model <judge_model> \
--questions_path <questions_path> \
--candidate_answers_path <candidate_answers_path> \
--ref_answers_path <ref_answers_path> \
--output_path <output_path>

To run a quick demo, use the following command to evaluate the candidate answers in example_data/candidate_answers.jsonl against the reference answers in example_data/ref_answers.jsonl using GPT-4 as the judge model.

mini_judge --output_path <output_path>

Data Format

All input data files are presumed to be in jsonl format.

The candidate_answers and ref_answers files should have each line as a json with an answer tag.

Similarly, the questions file should have json lines with a question tag.

All other tags will be ignored.

References

Lianmin Zheng, Wei-Lin Chiang, Ying Sheng, Siyuan Zhuang, Zhanghao Wu, Yonghao Zhuang, Zi Lin, Zhuohan Li, Dacheng Li, Eric. P Xing, Hao Zhang, Joseph E. Gonzalez, & Ion Stoica. (2023). Judging LLM-as-a-judge with MT-Bench and Chatbot Arena ArXiv.