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Critique Ability of Large Language Models: Data Generation

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Abstract and 1. Introduction

  1. Definition of Critique Ability

  2. Construction of CriticBench

    3.1 Data Generation

    3.2 Data Selection

  3. Properties of Critique Ability

    4.1 Scaling Law

    4.2 Self-Critique Ability

    4.3 Correlation to Certainty

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

  5. Conclusion, References, and Acknowledgments


A. Notations

B. CriticBench: Sources of Queries

C. CriticBench: Data Generation Details

D. CriticBench: Data Selection Details

E. CriticBench: Statistics and Examples

F. Evaluation Settings

Authors:

(1) Liangchen Luo, Google Research ([email protected]);

(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 ([email protected]).

This paper is available on arxiv under CC BY 4.0 DEED license.


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Topics

#large-language-models-(llms)#critical-thinking-in-ai#model-evaluation-framework#criticbench-benchmark#self-critique-and-enhancement#natural-language-processing#benchmarking-ai-performance#machine-learning-evaluation

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