The Critique of Critique

• URL: http://arxiv.org/abs/2401.04518v2
• Date: Sat, 1 Jun 2024 17:52:14 GMT
• Title: The Critique of Critique
• Authors: Shichao Sun, Junlong Li, Weizhe Yuan, Ruifeng Yuan, Wenjie Li, Pengfei Liu,
• Abstract summary: We pioneer the critique of critique, termed MetaCritique, which builds specific quantification criteria. We construct a meta-evaluation dataset covering 4 tasks involving human-written and LLM-generated critiques. Experiments demonstrate that MetaCritique can achieve near-human performance.
• Score: 45.40025444461465
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Critique, as a natural language description for assessing the quality of model-generated content, has played a vital role in the training, evaluation, and refinement of LLMs. However, a systematic method to evaluate the quality of critique is lacking. In this paper, we pioneer the critique of critique, termed MetaCritique, which builds specific quantification criteria. To achieve a reliable evaluation outcome, we propose Atomic Information Units (AIUs), which describe the critique in a more fine-grained manner. MetaCritique aggregates each AIU's judgment for the overall score. Moreover, MetaCritique delivers a natural language rationale for the intricate reasoning within each judgment. Lastly, we construct a meta-evaluation dataset covering 4 tasks across 16 public datasets involving human-written and LLM-generated critiques. Experiments demonstrate that MetaCritique can achieve near-human performance. Our study can facilitate future research in LLM critiques based on our following observations and released resources: (1) superior critiques judged by MetaCritique can lead to better refinements, indicating that it can potentially enhance the alignment of existing LLMs; (2) the leaderboard of critique models reveals that open-source critique models commonly suffer from factuality issues; (3) relevant code and data are publicly available at https://github.com/GAIR-NLP/MetaCritique to support deeper exploration; (4) an API at PyPI with the usage documentation in Appendix C allows users to assess the critique conveniently.

Related papers

• RealCritic: Towards Effectiveness-Driven Evaluation of Language Model Critiques [59.861013614500024]
  We introduce a new benchmark designed to assess the critique capabilities of Large Language Models (LLMs) Unlike existing benchmarks, which typically function in an open-loop fashion, our approach employs a closed-loop methodology that evaluates the quality of corrections generated from critiques.
  arXiv  Detail & Related papers  (2025-01-24T13:48:10Z)
• Enabling Scalable Oversight via Self-Evolving Critic [59.861013614500024]
  SCRIT (Self-evolving CRITic) is a framework that enables genuine self-evolution of critique abilities. It self-improves by training on synthetic data, generated by a contrastive-based self-critic. It achieves up to a 10.3% improvement on critique-correction and error identification benchmarks.
  arXiv  Detail & Related papers  (2025-01-10T05:51:52Z)
• CriticEval: Evaluating Large Language Model as Critic [110.29766259843453]
  CriticEval is a novel benchmark designed to comprehensively and reliably evaluate critique ability of Large Language Models. To ensure the comprehensiveness, CriticEval evaluates critique ability from four dimensions across nine diverse task scenarios. To ensure the reliability, a large number of critiques are annotated to serve as references.
  arXiv  Detail & Related papers  (2024-02-21T12:38:59Z)
• CritiqueLLM: Towards an Informative Critique Generation Model for Evaluation of Large Language Model Generation [87.44350003888646]
  Eval-Instruct can acquire pointwise grading critiques with pseudo references and revise these critiques via multi-path prompting. CritiqueLLM is empirically shown to outperform ChatGPT and all the open-source baselines.
  arXiv  Detail & Related papers  (2023-11-30T16:52:42Z)
• Exploring the Reliability of Large Language Models as Customized Evaluators for Diverse NLP Tasks [65.69651759036535]
  We analyze whether large language models (LLMs) can serve as reliable alternatives to humans. This paper explores both conventional tasks (e.g., story generation) and alignment tasks (e.g., math reasoning) We find that LLM evaluators can generate unnecessary criteria or omit crucial criteria, resulting in a slight deviation from the experts.
  arXiv  Detail & Related papers  (2023-10-30T17:04:35Z)
• Critique Ability of Large Language Models [38.34144195927209]
  This study explores the ability of large language models (LLMs) to deliver accurate critiques across various tasks. We develop a benchmark called CriticBench, which comprises 3K high-quality natural language queries and corresponding model responses.
  arXiv  Detail & Related papers  (2023-10-07T14:12:15Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.