---
title: "CritiqueLLM — paper by Ke et al. (2023) — summary and ideas"
description: "This paper presents Eval-Instruct, a pipeline for building more informative training data for critique… 51 references, cited by 65 papers here."
authors: ["Pei Ke", "Bosi Wen", "Zhuoer Feng", "Xiao Liu", "Xuanyu Lei", "Jiale Cheng", "Shengyuan Wang", "Aohan Zeng", "Yuxiao Dong", "Hongning Wang", "Jie Tang", "Minlie Huang"]
year: 2023
venue: "arXiv:2311.18702"
source: "https://arxiv.org/abs/2311.18702"
doi: "10.48550/arxiv.2311.18702"
arxiv: "2311.18702"
licence: "arXiv — non-exclusive licence to distribute"
licence_url: "http://arxiv.org/licenses/nonexclusive-distrib/1.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/ke-2023-critiquellm-informative-critique-generation/"
---
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# CritiqueLLM: Towards an Informative Critique Generation Model for Evaluation of Large Language Model Generation

**Authors:** [Pei Ke](https://md.research.elamur.ai/free/en/authors/pei-ke/), [Bosi Wen](https://md.research.elamur.ai/free/en/authors/bosi-wen/), [Zhuoer Feng](https://md.research.elamur.ai/free/en/authors/zhuoer-feng/), [Xiao Liu](https://md.research.elamur.ai/free/en/authors/xiao-liu/), [Xuanyu Lei](https://md.research.elamur.ai/free/en/authors/xuanyu-lei/), [Jiale Cheng](https://md.research.elamur.ai/free/en/authors/jiale-cheng/), [Shengyuan Wang](https://md.research.elamur.ai/free/en/authors/shengyuan-wang/), [Aohan Zeng](https://md.research.elamur.ai/free/en/authors/aohan-zeng/), [Yuxiao Dong](https://md.research.elamur.ai/free/en/authors/yuxiao-dong/), [Hongning Wang](https://md.research.elamur.ai/free/en/authors/hongning-wang/), [Jie Tang](https://md.research.elamur.ai/free/en/authors/jie-tang/) and [Minlie Huang](https://md.research.elamur.ai/free/en/authors/minlie-huang/) · **Year:** 2023 · **Venue:** arXiv:2311.18702 · **Licence:** [arXiv — non-exclusive licence to distribute](http://arxiv.org/licenses/nonexclusive-distrib/1.0/)

**Links:** [Publisher's page](https://arxiv.org/abs/2311.18702) · [PDF](https://arxiv.org/pdf/2311.18702) · [DOI 10.48550/arxiv.2311.18702](https://doi.org/10.48550/arxiv.2311.18702) · [arXiv 2311.18702](https://arxiv.org/abs/2311.18702) · [OpenAlex](https://openalex.org/W4389261394)

**Citations:** 3 citations (OpenAlex, 2026-10-14) · [65](https://md.research.elamur.ai/free/en/papers/ke-2023-critiquellm-informative-critique-generation/cited-by/) in our library, of the 141,840 papers whose bibliographies we read

## Summary
LLM-based evaluators trained on critiques that GPT-4 produces through direct prompting often give vague, generic judgments. The problem is worst when no reference answer is available. This paper presents Eval-Instruct, a pipeline for building more informative training data for critique generation.

The authors first collect user queries spanning ten instruction-following task types and gather responses from ten LLMs. Manually verified GPT-4 outputs serve as pseudo references. GPT-4 then writes detailed referenced pointwise critiques. These critiques are converted into the other evaluation formats through multi-path prompting. One strategy turns pointwise critiques into pairwise ones. The other rewrites referenced critiques so they no longer rely on the reference. Because reference-free pairwise data can be reached by two different paths, inconsistent samples are discarded via cross validation.

A single model, CritiqueLLM (built on ChatGLM3-6B), is fine-tuned on all four task/setting combinations. It surpasses ChatGPT and open-source judges and matches GPT-4 on system-level correlations for pointwise grading. Its critiques also help ChatGPT improve its own answers, which makes it a cheaper, locally runnable alternative to API-based evaluation.

## Key points
- The data combines 10 task categories with responses from 10 models, including GPT-4, ChatGPT, ChatGLM, MOSS, Baichuan2-13B-Chat and Ernie Bot. Human-corrected GPT-4 answers serve as pseudo references.
- Eval-Instruct relies on two prompting strategies: Pointwise-to-Pairwise and Referenced-to-Reference-Free. A cross-validation filter is applied to reference-free pairwise data, and removing it mainly hurts pairwise performance.
- On AlignBench, CritiqueLLM reaches text-level Pearson of about 0.555 with references and 0.366 without them. Self-consistency decoding raises these to 0.573 and 0.384.
- Training without explanations lowers Pearson to 0.509 and 0.332, which shows the explanations play a role similar to chain-of-thought reasoning.
- When used as feedback for revising ChatGPT outputs, CritiqueLLM's critiques yield gains close to those from GPT-4's critiques. ChatGPT's own self-critiques, by contrast, reduced quality. Code is available at github.com/thu-coai/CritiqueLLM.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

## Institutions

- [Tsinghua University](https://md.research.elamur.ai/free/en/universities/tsinghua-university/) — University, Beijing, China · 137 works · 83,262 citations
- [Zhipu AI](https://md.research.elamur.ai/free/en/companies/zhipu-ai/) — Company, Beijing, China · 12 works · 2,069 citations

📚 Shelves (4): [🖥️ Computer Science](https://md.research.elamur.ai/free/en/shelves/computer-science/) · 9,086 works · [🤖 Artificial Intelligence](https://md.research.elamur.ai/free/en/shelves/computer-science/artificial-intelligence/) · 8,889 works · [🎭 Arts and Humanities](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/) · 9,296 works · [🗣️ Language and Linguistics](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/language-and-linguistics/) · 9,206 works

🧭 Topics (1): [🧠 Language models & pretraining](https://md.research.elamur.ai/free/en/topics/language-models/) · 1,343 works

## Cite

Cite as: Pei Ke, Bosi Wen, Zhuoer Feng, Xiao Liu, Xuanyu Lei, Jiale Cheng, Shengyuan Wang, Aohan Zeng, Yuxiao Dong, Hongning Wang, Jie Tang and Minlie Huang. 2023. CritiqueLLM: Towards an Informative Critique Generation Model for Evaluation of Large Language Model Generation. arXiv:2311.18702.

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@misc{ke2023critiquellm,
  title = {{CritiqueLLM: Towards an Informative Critique Generation Model for Evaluation of Large Language Model Generation}},
  author = {Pei Ke, Bosi Wen, Zhuoer Feng, Xiao Liu, Xuanyu Lei, Jiale Cheng, Shengyuan Wang, Aohan Zeng, Yuxiao Dong, Hongning Wang, Jie Tang and Minlie Huang},
  year = {2023},
  doi = {10.48550/arxiv.2311.18702},
  eprint = {2311.18702},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.48550/arxiv.2311.18702}
}
```

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*Updated 24 October 2026*