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://anonymous.4open.science/r/MetaCritique-ARR/ to support deeper exploration; (4) an API at PyPI with the usage documentation in Appendix C allows users to assess the critique conveniently.- Anthology ID:
- 2024.findings-acl.538
- Volume:
- Findings of the Association for Computational Linguistics: ACL 2024
- Month:
- August
- Year:
- 2024
- Address:
- Bangkok, Thailand
- Editors:
- Lun-Wei Ku, Andre Martins, Vivek Srikumar
- Venue:
- Findings
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 9077–9096
- Language:
- URL:
- https://aclanthology.org/2024.findings-acl.538
- DOI:
- 10.18653/v1/2024.findings-acl.538
- Cite (ACL):
- Shichao Sun, Junlong Li, Weizhe Yuan, Ruifeng Yuan, Wenjie Li, and Pengfei Liu. 2024. The Critique of Critique. In Findings of the Association for Computational Linguistics: ACL 2024, pages 9077–9096, Bangkok, Thailand. Association for Computational Linguistics.
- Cite (Informal):
- The Critique of Critique (Sun et al., Findings 2024)
- PDF:
- https://preview.aclanthology.org/add_acl24_videos/2024.findings-acl.538.pdf