Table of Links Abstract and 1. Introduction Abstract and 1. Introduction Definition of Critique Ability


Construction of CriticBench
3.1 Data Generation
3.2 Data Selection


Properties of Critique Ability
4.1 Scaling Law
4.2 Self-Critique Ability
4.3 Correlation to Certainty


New Capacity with Critique: Self-Consistency with Self-Check


Conclusion, References, and Acknowledgments Definition of Critique Ability Definition of Critique Ability Definition of Critique Ability Construction of CriticBench
3.1 Data Generation
3.2 Data Selection Construction of CriticBench Construction of CriticBench 3.1 Data Generation 3.1 Data Generation 3.2 Data Selection 3.2 Data Selection Properties of Critique Ability
4.1 Scaling Law
4.2 Self-Critique Ability
4.3 Correlation to Certainty Properties of Critique Ability Properties of Critique Ability 4.1 Scaling Law 4.1 Scaling Law 4.2 Self-Critique Ability 4.2 Self-Critique Ability 4.3 Correlation to Certainty 4.3 Correlation to Certainty New Capacity with Critique: Self-Consistency with Self-Check New Capacity with Critique: Self-Consistency with Self-Check New Capacity with Critique: Self-Consistency with Self-Check Conclusion, References, and Acknowledgments Conclusion, References, and Acknowledgments Conclusion, References, and Acknowledgments A. Notations A. Notations B. CriticBench: Sources of Queries B. CriticBench: Sources of Queries C. CriticBench: Data Generation Details C. CriticBench: Data Generation Details D. CriticBench: Data Selection Details D. CriticBench: Data Selection Details E. CriticBench: Statistics and Examples E. CriticBench: Statistics and Examples F. Evaluation Settings F. Evaluation Settings 5 NEW CAPACITY WITH CRITIQUE: SELF-CONSISTENCY WITH SELF-CHECK To explore the new capacity with critique ability, we would like to introduce a straightforward yet effective baseline to demonstrate the potential of leveraging the critique ability to improve model performance. The idea is intuitive: drawing a parallel to humans participating in a contest — where they typically check their most uncertain answers before submission to identify and correct mistakes — we suggest a similar process can be emulated in language models. This can be accomplished by prompting the models to confirm the accuracy of their generated answers before presenting them. We assess the performance of PaLM-2, ChatGPT and GPT-4 on the GSM8K dataset using the selfconsistency with self-check method, as presented in Table 1. We use a certainty threshold of C = 2 for GPT-4 and C = 1 for both PaLM-2 and ChatGPT. Compared to self-consistency baselines, the additional self-check procedure achieves 3.03%, 16.09%, and 9.52% error reduction rate for ChatGPT, PalM-2 and GPT-4 respectively, highlighting the value of critique ability. It is noted that our primary objective of this paper is to explore the concept and attributes of critique ability, rather than advancing the state of the art. Thus, we opt to stick with the prompting-based critic model for the sake of simplicity. While fine-tuning the critic model or using critiques to supervise the policy model could potentially push the scores higher, such enhancements are not the focus of this study. We believe future work in this direction can further improve the performance. 6 CONCLUSION In this work, we conduct a study exploring critique abilities of LLMs across various tasks. Evaluation results of multiple widely-used LLMs on the proposed CRITICBENCH reveal that: most LLMs find critique challenging, especially self-critique. 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ACKNOWLEDGMENTS We thank Pengchen Yin, Eric Li, Zihan Wang, Le Hou and Yuexin Wu for helpful discussions. Authors:
(1) Liangchen Luo, Google Research (luolc@google.com);
(2) Zi Lin, UC San Diego;
(3) Yinxiao Liu, Google Research;
(4) Yun Zhu, Google Research;
(5) Jingbo Shang, UC San Diego;
(6) Lei Meng, Google Research (leimeng@google.com). Authors: Authors: (1) Liangchen Luo, Google Research (luolc@google.com); (2) Zi Lin, UC San Diego; (3) Yinxiao Liu, Google Research; (4) Yun Zhu, Google Research; (5) Jingbo Shang, UC San Diego; (6) Lei Meng, Google Research (leimeng@google.com). This paper is available on arxiv under CC BY 4.0 DEED license. This paper is available on arxiv under CC BY 4.0 DEED license. available on arxiv available on arxiv

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