This repo contains source code for the paper: JudgeBench: A Benchmark for Evaluating LLM-Based Judges.
📃 [Paper] • 💻 [Github] • 🤗 [Dataset] • 🏆 [Leaderboard]
Set up a new environment and run pip install -r requirements.txt. This repository has been built and tested with Python 3.10.12.
The datasets are contained in data/, including 350 unique response pairs generated by GPT-4o and 270 unique response pairs generated by Claude-3.5-Sonnet. Each instance is a JSON object with the following format:
{
"pair_id": "81ec57f2-483f-515f-93ff-78a8910b2153", # unique identifier for response pair
"original_id": "10646", # original question id in the source dataset
"source": "mmlu-pro-computer science", # source dataset for question
"question": "Consider an additive white Gaussian noise channel ...", # question to which responses are generated
"response_model": "gpt-4o-2024-05-13", # model used to generate the responses
"response_A": "To determine the capacity of an additive white ...", # one response candidate
"response_B": "We are given an additive white Gaussian noise ...", # another response candidate
"label": "B>A" # objective label indicating correctness
}Our dataset is also available on HuggingFace and can be accessed using the Datasets library! The data on huggingface follows the same format outlined above.
from datasets import load_dataset
gpt_data = load_dataset("ScalerLab/JudgeBench", split="gpt")
claude_data = load_dataset("ScalerLab/JudgeBench", split="claude")The main functionality for running a judge on JudgeBench is located in run_judge.py. Additional utilities and helper functions are located under utils/. Currently, this repository supports several prompted judges and fine-tuned judges: Vanilla, Arena-Hard, PandaLM, Prometheus 2, JudgeLM, AutoJ, and Skywork-Critic. We also support the following reward models: InternLM2-7B-Reward, InternLM2-20B-Reward, GRM-Gemma-2B, Skywork-Reward-Gemma-2-27B, and Skywork-Reward-Llama-3.1-8B. As a simple example, to run the Arena-Hard Judge powered by GPT-4o-mini on the GPT-4o response pairs, run:
export OPENAI_API_KEY=your-api-key-here
python run_judge.py --judge_name arena_hard --judge_model gpt-4o-mini --pairs data/dataset=judgebench,response_model=gpt-4o-2024-05-13.jsonl
run_judge.py takes the following command line arguments:
| argument | description | required | options |
|---|---|---|---|
--judge_name |
Name of judge. | Yes | vanilla, arena_hard, panda_lm, prometheus_2, judge_lm, auto_j, skywork_critic, reward_model |
--judge_model |
Name of model to use with judge. | Yes | str (e.g., gpt-4o-mini, meta-llama/Llama-3.1-8B-Instruct) |
--single_game |
By default, we run each pair through twice with different response orderings. This flag will only run the original ordering, and should be used for judges that are order-independent (e.g., reward models). | No (default is False) | bool |
--seed |
Random seed to use for any random operations. | No (default is 42) | int |
--concurrency_limit |
Asyncio can be used to generate judgments concurrently. This argument sets an upper limit on the number of pairs judgments are generated for at any given time. | No (default is 1) | int |
--pairs |
Path to dataset. This does not necessarily need to be a JudgeBench dataset, as long as it adheres to the format outlined above. | Yes | str |
Our codebase support models hosted by OpenAI, Anthropic, Together, and Google Cloud's VertexAI. Simply export an appropriate API key as shown below. For VertexAI, see the authorization instructions in utils/models.py.
export OPENAI_API_KEY=your-openai-api-key # (GPT-4o, GPT-4o-mini, o1-preview, o1-mini, etc.)
export ANTHROPIC_API_KEY=your-anthropic-api-key # (Claude-3.5-Sonnet, Claude-3-Haiku, etc.)
export TOGETHER_API_KEY=your-together-api-key # (Llama-3.1-405B-Instruct, etc.)
Then run run_judge.py. For example:
python run_judge.py --judge_name arena_hard --judge_model gpt-4o-mini --pairs data/dataset=judgebench,response_model=gpt-4o-2024-05-13.jsonl
This will print out an evaluation report, and save an output file under outputs/ containing the judgments for each pair.
Additionally, we also support models served via a locally-running vLLM OpenAI compatible server. See vLLM's official documentation for more information. To serve a model locally on a machine with enough GPU compute, run:
docker run --runtime nvidia --gpus all \
-v ~/.cache/huggingface:/root/.cache/huggingface \
--env "HF_TOKEN=<your-huggingface-token>" \
-p 8000:8000 \
--ipc=host \
vllm/vllm-openai:latest \
--model Skywork/Skywork-Critic-Llama-3.1-8B
Then, from either the same machine (or a different computer connected to the machine serving the model via an ssh tunnel over port 8000), run run_judge.py as normal. For example:
python run_judge.py --judge_name skywork_critic --judge_model Skywork/Skywork-Critic-Llama-3.1-8B --pairs data/dataset=judgebench,response_model=gpt-4o-2024-05-13.jsonl
Similarly, we support running reward models locally, but only on machines with enough GPU compute (ssh tunnelling is not an option here). To install the additional libraries required to do so, run pip install -r requirements-cuda.txt into an environment. Then to run a reward model on JudgeBench, run:
python run_judge.py --judge_name reward_model --judge_model Skywork/Skywork-Reward-Llama-3.1-8B --single_game --pairs data/dataset=judgebench,response_model=gpt-4o-2024-05-13.jsonl
Note that all the reward models we have implemented share a judge_name of reward_model, while the judge_model is the identifier on HuggingFace. Also, reward models can be run with the --single_game flag to save computation, since reward models assign a score to each model independently.
Start by creating a new judge class in utils/judges.py that inherits from the abstract class Judge.
class Judge(ABC):
@abstractmethod
async def get_judgment(self, question: str, answer_A: str, answer_B: str) -> Dict[str, Any]:
passThe only required method for a judge is get_judgment(), which takes in a single question and two ordered candidate responses (response_A and response_B) and returns a dictionary containing the key decision indicating the verdict (e.g., A>B or B>A). For convenience, we often return other items (e.g., input prompt, generated judgement) under the judgment key. Moreover, we tend to use the judge's __init__() method to initialize the api client, and a helper method or two to abstract away some implementation details (e.g., parsing the decision from the judgment). For example implementations, see utils/judges.py
Prompts for judges can be stored under utils/templates/ in jinja format, then formatted directly from get_judgment() using the jinja file's name. For example,
formatted_prompt = prompts.render_template("arena_hard_judge_prompt", prompt=question, answer_a=answer_A, answer_b=answer_B)Lastly, add an entry for your new judge to get_judge_from_judge_name_and_model in utils/judges.py. For example:
def get_judge_from_judge_name_and_model(judge_name: str, judge_model: str) -> Judge:
if judge_name == "arena_hard":
return ArenaHard(judge_model)
elif judge_name == "my-new-judge":
return MyNewJudge(judge_model)
else:
raise NotImplementedError(f"Judge with name {judge_name} is not yet implemented.")Please open an issue here on GitHub! If you'd like us to add your Judge's results to the JudgeBench leaderboard, please make a PR with your judge's implementation and open an issue referencing your PR. If you get stuck, we'd be glad to try and assist!
If you find JudgeBench useful or relevant to your work, please kindly cite our paper:
@misc{judgebench2024,
title={JudgeBench: A Benchmark for Evaluating LLM-Based Judges},
author={Sijun Tan and Siyuan Zhuang and Kyle Montgomery and Willian Y. Tang and Alejandro Cuadron and Chenguang Wang and Raluca Ada Popa and Ion Stoica},
year={2024},
archivePrefix={arXiv},
url={https://arxiv.org/abs/2410.12784}
}