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.

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