# PRE: A Peer Review Based Large Language Model Evaluator

arXiv: https://arxiv.org/abs/2401.15641 · HTML: https://mlsanity.com/paper/2401.15641/

## Key facts

- Cited by 28 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 56 references that are in the map.
- First posted on arXiv 28 Jan 2024.

## Builds on (most cited first, 24 of 24)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Training language models to follow instructions with human feedback](https://mlsanity.com/paper/2203.02155/) (2022)
- [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023)
- [Llama 2: Open Foundation and Fine-Tuned Chat Models](https://mlsanity.com/paper/2307.09288/) (2023)
- [Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena](https://mlsanity.com/paper/2306.05685/) (2023)
- [Measuring Massive Multitask Language Understanding](https://mlsanity.com/paper/2009.03300/) (2020)
- [BERTScore: Evaluating Text Generation with BERT](https://mlsanity.com/paper/1904.09675/) (2019)
- [PaLM: Scaling Language Modeling with Pathways](https://mlsanity.com/paper/2204.02311/) (2022)
- [Prefix-Tuning: Optimizing Continuous Prompts for Generation](https://mlsanity.com/paper/2101.00190/) (2021)
- [Scaling Instruction-Finetuned Language Models](https://mlsanity.com/paper/2210.11416/) (2022)
- [DeBERTa: Decoding-enhanced BERT with Disentangled Attention](https://mlsanity.com/paper/2006.03654/) (2020)
- [Don't Give Me the Details, Just the Summary! Topic-Aware Convolutional Neural Networks for Extreme Summarization](https://mlsanity.com/paper/1808.08745/) (2018)
- [GLM-130B: An Open Bilingual Pre-trained Model](https://mlsanity.com/paper/2210.02414/) (2022)
- [RWKV: Reinventing RNNs for the Transformer Era](https://mlsanity.com/paper/2305.13048/) (2023)
- [ChatEval: Towards Better LLM-based Evaluators through Multi-Agent Debate](https://mlsanity.com/paper/2308.07201/) (2023)
- [Baichuan 2: Open Large-scale Language Models](https://mlsanity.com/paper/2309.10305/) (2023)
- [Evaluating the Factual Consistency of Abstractive Text Summarization](https://mlsanity.com/paper/1910.12840/) (2019)
- [PandaLM: An Automatic Evaluation Benchmark for LLM Instruction Tuning Optimization](https://mlsanity.com/paper/2306.05087/) (2023)
- [Evaluating the Performance of Large Language Models on GAOKAO Benchmark](https://mlsanity.com/paper/2305.12474/) (2023)
- [PRD: Peer Rank and Discussion Improve Large Language Model based Evaluations](https://mlsanity.com/paper/2307.02762/) (2023)
- [GPTEval: A Survey on Assessments of ChatGPT and GPT-4](https://mlsanity.com/paper/2308.12488/) (2023)
- [Xiezhi: An Ever-Updating Benchmark for Holistic Domain Knowledge Evaluation](https://mlsanity.com/paper/2306.05783/) (2023)
- [SuperCLUE: A Comprehensive Chinese Large Language Model Benchmark](https://mlsanity.com/paper/2307.15020/) (2023)
- [Model-agnostic vs. Model-intrinsic Interpretability for Explainable Product Search](https://mlsanity.com/paper/2108.05317/) (2021)

## Built on by (most cited first, 28 of 28)

- [LLMs-as-Judges: A Comprehensive Survey on LLM-based Evaluation Methods](https://mlsanity.com/paper/2412.05579/) (2024)
- [Bias and Unfairness in Information Retrieval Systems: New Challenges in the LLM Era](https://mlsanity.com/paper/2404.11457/) (2024)
- [LegalAgentBench: Evaluating LLM Agents in Legal Domain](https://mlsanity.com/paper/2412.17259/) (2024)
- [Auto-Arena: Automating LLM Evaluations with Agent Peer Battles and Committee Discussions](https://mlsanity.com/paper/2405.20267/) (2024)
- [LexEval: A Comprehensive Chinese Legal Benchmark for Evaluating Large Language Models](https://mlsanity.com/paper/2409.20288/) (2024)
- [CalibraEval: Calibrating Prediction Distribution to Mitigate Selection Bias in LLMs-as-Judges](https://mlsanity.com/paper/2410.15393/) (2024)
- [Multi-Agent-as-Judge: Aligning LLM-Agent-Based Automated Evaluation with Multi-Dimensional Human Evaluation](https://mlsanity.com/paper/2507.21028/) (2025)
- [Toward Generalizable Evaluation in the LLM Era: A Survey Beyond Benchmarks](https://mlsanity.com/paper/2504.18838/) (2025)
- [LexRAG: Benchmarking Retrieval-Augmented Generation in Multi-Turn Legal Consultation Conversation](https://mlsanity.com/paper/2502.20640/) (2025)
- [BLADE: Enhancing Black-box Large Language Models with Small Domain-Specific Models](https://mlsanity.com/paper/2403.18365/) (2024)
- [A User-Centric Multi-Intent Benchmark for Evaluating Large Language Models](https://mlsanity.com/paper/2404.13940/) (2024)
- [PiCO: Peer Review in LLMs based on the Consistency Optimization](https://mlsanity.com/paper/2402.01830/) (2024)
- [Learning from Committee: Reasoning Distillation from a Mixture of Teachers with Peer-Review](https://mlsanity.com/paper/2410.03663/) (2024)
- [Verdict: A Library for Scaling Judge-Time Compute](https://mlsanity.com/paper/2502.18018/) (2025)
- [UPME: An Unsupervised Peer Review Framework for Multimodal Large Language Model Evaluation](https://mlsanity.com/paper/2503.14941/) (2025)
- [DeepReviewer 2.0: A Traceable Agentic System for Auditable Scientific Peer Review](https://mlsanity.com/paper/2604.09590/) (2026)
- [A Survey of RWKV](https://mlsanity.com/paper/2412.14847/) (2024)
- [Benchmarking LLM-as-a-Judge for Long-Form Output Evaluation](https://mlsanity.com/paper/2606.01629/) (2026)
- [Scoring, Reasoning, and Selecting the Best! Ensembling Large Language Models via a Peer-Review Process](https://mlsanity.com/paper/2512.23213/) (2025)
- [PeerRank: Autonomous LLM Evaluation Through Web-Grounded, Bias-Controlled Peer Review](https://mlsanity.com/paper/2602.02589/) (2026)
- [Can ChatGPT Really Understand Modern Chinese Poetry?](https://mlsanity.com/paper/2603.20851/) (2026)
- [Auto-PRE: An Automatic and Cost-Efficient Peer-Review Framework for Language Generation Evaluation](https://mlsanity.com/paper/2410.12265/) (2024)
- [ChemActor: Enhancing Automated Extraction of Chemical Synthesis Actions with LLM-Generated Data](https://mlsanity.com/paper/2506.23520/) (2025)
- [AraTable: Benchmarking LLMs' Reasoning and Understanding of Arabic Tabular Data](https://mlsanity.com/paper/2507.18442/) (2025)
- [ReasFlow: Assisting Reasoning-Centric Scientific Discovery in Applied Mathematics via a Knowledge-Based Multi-Agent System](https://mlsanity.com/paper/2607.14178/) (2026)
- [AI Predicts AGI: Leveraging AGI Forecasting and Peer Review to Explore LLMs' Complex Reasoning Capabilities](https://mlsanity.com/paper/2412.09385/) (2024)
- [LLMs Judge Themselves: A Game-Theoretic Framework for Human-Aligned Evaluation](https://mlsanity.com/paper/2510.15746/) (2025)
- [Poller: Are LLMs Suitable for Evaluating the Poetry Understanding Task?](https://mlsanity.com/paper/2606.30556/) (2026)

## Abstract (arXiv)

The impressive performance of large language models (LLMs) has attracted considerable attention from the academic and industrial communities. Besides how to construct and train LLMs, how to effectively evaluate and compare the capacity of LLMs has also been well recognized as an important yet difficult problem. Existing paradigms rely on either human annotators or model-based evaluators to evaluate the performance of LLMs on different tasks. However, these paradigms often suffer from high cost, low generalizability, and inherited biases in practice, which make them incapable of supporting the sustainable development of LLMs in long term. In order to address these issues, inspired by the peer review systems widely used in academic publication process, we propose a novel framework that can automatically evaluate LLMs through a peer-review process. Specifically, for the evaluation of a specific task, we first construct a small qualification exam to select "reviewers" from a couple of powerful LLMs. Then, to actually evaluate the "submissions" written by different candidate LLMs, i.e., the evaluatees, we use the reviewer LLMs to rate or compare the submissions. The final ranking of evaluatee LLMs is generated based on the results provided by all reviewers. We conducted extensive experiments on text summarization tasks with eleven LLMs including GPT-4. The results demonstrate the existence of biasness when evaluating using a single LLM. Also, our PRE model outperforms all the baselines, illustrating the effectiveness of the peer review mechanism.