Skip to content

Latest commit

 

History

History
192 lines (155 loc) · 8.19 KB

README.md

File metadata and controls

192 lines (155 loc) · 8.19 KB
Raw

Peer-review-in-LLMs: Automatic Evaluation Method for LLMs in Open-environment

If you like our project, please give us a star ⭐ on GitHub for latest update.

arXiv License

🤗 Brief Intro

Existing large language models (LLMs) evaluation methods typically focus on testing the performance on some closed-environment and domain-specific benchmarks with human annotations. We explore a novel unsupervised evaluation direction, utilizing peer-review mechanisms to measure LLMs automatically. In this setting, both open-source and closed-source LLMs lie in the same environment, capable of answering unlabeled questions and evaluating each other. To obtain the ability hierarchy among these models, we assign each LLM a learnable capability parameter to adjust the final ranking.We formalize it as a constrained optimization problem, intending to maximize the consistency of each LLM's capabilities and scores. The key assumption behind is that high-level LLM can evaluate others' answers more accurately than low-level ones, while higher-level LLM can also achieve higher response scores.

😮 Highlights

💡 A novel LLM automatic evaluation direction

Peer-review-in-LLMs is a novel LLM automatic evaluation direction without human feedback, utilizing peer-review mechanisms to measure LLMs automatically. All LLMs can answer unlabeled questions and evaluate each other.

⚡️ A constrained optimization based on the consistency assumption

A constrained optimization based on the consistency assumption is proposed to re-rank the LLMs to be closer to human rankings.

  • The key assumption is that high-level LLM can evaluate others’ answers more accurately (confidence) than low-level ones, while higher-level LLM can also achieve higher answer-ranking scores.

🔥 Three metrics for evaluating the gap with human preferences

We propose three metrics called PEN (Permutation Entropy), CIN(Count Inversions), and LIS(Longest Increasing Subsequence) on the peer-review-in-LLMs framework for evaluating the gap with human preferences.

🤗 The Pipeline of Peer-review-in-LLMs

Our 'Peer-review-in-LLMs' framework integrates peer review with LLMs for response evaluation. It unfolds in two phases: first, LLMs generate and peer-review responses to questions, ranking them by confidence. Next, we refine these weights through consistency optimization, aiming to minimize the system's entropy, thereby achieving the final ranking

🚀 Main Results

Our experimental results, derived from tests conducted on the Chatbot Arena, MT-Bench, and Alpaca-Farm datasets. Across all three datasets, our Peer Review method (Ours) outperformed the other three settings on all metrics, demonstrating its robustness and generalizability across different datasets.

  • Chatbot Arena

  • MT-Bench

  • Alpaca-Farm

🛠️ Quick Start

🔧 Requirements and Installation

Clone this repo and install the requirements.

$ git clone https://github.com/PKU-YuanGroup/Peer-review-in-LLMs
$ cd Peer-review-in-LLMs
$ pip install --upgrade pip
$ pip install -r requirements.txt

📝 Download Data (if required)

You can directly use our complete dataset by downloading it from this link:

https://drive.google.com/drive/folders/1c2tDhaGiuwrtwja05EFVMZCweXey86sM?usp=sharing.

After downloading, place the 'mt_bench' folder into the 'data' folder. Alternatively, you can simply run the Peer review code in its entirety.

🖥️ Demo

After downloading the data, you can directly run main.py to experience the optimization process of Consistency Optimization using the review results from 15 models on the mt_bench dataset.

$ python main.py

✒️ LLMs response, assign and judge

This code is executed within the 'llm_judge' folder.

$ cd Peer-review-in-LLMs/llm_judge

Retrieve LLM Responses

Generate model responses

$ python gen_model_answer.py \
--model-path [/path/to/model] \
--model-id [MODEL-ID] \
--bench-name mt_bench

If using proprietary models such as GPT-3.5 or GPT-4 as reviewers, you need to enter the key first.

$ export OPENAI_API_KEY=XXXXXX  # set the OpenAI API key
$ python gen_model_answer.py \
--model-id [MODEL-ID] \
--bench-name mt_bench

Arguments:

  • [MODEL-PATH] is the path to the weights, which can be a local folder or a Hugging Face repo ID.
  • [MODEL-ID] is a name you give to the model.
  • [BENCH-NAME] is the name the dataset you use

e.g.,

python gen_model_answer.py --model-path PKU-YuanGroup/vicuna-7b-v1.5 --model-id vicuna-7b-v1.5 --bench-name mt_bench

Assign Reviewers to LLM Battles

After retrieving all responses from the models to be evaluated, pair the responses and assign Reviewers.

$ python assign_judge.py

Review the Battle Pairs

After assigning reviewers, review the battle pairs and output the judgment.

$ python gen_judgment.py \
--mode pairwise-all \
--new-judge-model [MODEL-ID] \
--model-list [LIST-OF-MODEL-ID]\ 
--batch-size [BATCH-SIZE] \
--bench-name mt_bench \
--judge-model-path [/path/to/model]

If using proprietary models such as GPT-3.5 or GPT-4 as reviewers, you need to enter the key first.

$ export OPENAI_API_KEY=XXXXXX  # set the OpenAI API key
$ python gen_judgment.py \
--mode pairwise-all \
--new-judge-model [MODEL-ID] \
--model-list [LIST-OF-MODEL-ID]\ 
--bench-name mt_bench

Arguments:

  • [MODE] is the method of conducting reviews.
  • [MODEL-ID] is a name you give to the model.
  • [LIST-OF-MODEL-ID] is the list of models being reviewed.
  • [BATCH-SIZE] is the batch size for processing at one time.
  • [BENCH-NAME] is the name the dataset you use
  • [JUDGE-MODEL-PATH] is the path to the weights. e.g.,
$ python gen_judgment.py \
--mode pairwise-all \
--new-judge-model vicuna-7b-v1.5 \
--model-list WizardLM-13B-V1.2 gpt4all-13b-snoozy oasst-sft-4-pythia-12b-epoch-3.5 vicuna-7b-v1.5 chatglm-6b guanaco-33b-merged stablelm-tuned-alpha-7b gpt-3.5-turbo koala-13b llama-13b fastchat-t5-3b-v1.0 dolly-v2-12b vicuna-13b-v1.5 alpaca-13b mpt-7b-chat \ 
--batch-size 512 \
--bench-name mt_bench \
--judge-model-path PKU-YuanGroup/vicuna-7b-v1.5

⚖️ Consistency Optimization

The code for the Consistency Optimization stage is executed within the 'Peer-review-in-LLMs/con_optimization' folder, and the results are saved in the 'log' folder.

$ python main_ablation.py \
--baseline [BASELINE] \
--mode [MODE] \
--epoch [EPOCH]

Arguments:

  • [BASELINE] determines whether to run the baseline. If set to 0, it runs Peer-review (Ours).
  • [MODE] selects the baseline mode, with three options: Reversed, Uniform, and Order.
  • [EPOCH] is the number of epochs for conducting Peer-review (Ours)

✏️ Citation

If you find our paper and code useful in your research, please consider giving a star ⭐ and citation 📝.

@misc{ning2024peerreviewinllms,
      title={Peer-review-in-LLMs: Automatic Evaluation Method for LLMs in Open-environment}, 
      author={Kun-Peng Ning and Shuo Yang and Yu-Yang Liu and Jia-Yu Yao and Zhen-Hui Liu and Yu Wang and Ming Pang and Li Yuan},
      year={2024},
      eprint={2402.01830},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}