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 F EVALUATION SETTINGS To evaluate large language models on CRITICBENCH, we employ few-shot chain-of-thought prompting, rather than zero-shot. We choose few-shot because it is applicable to both pretrained and instruction-tuned checkpoints, whereas zero-shot may underestimate the capabilities of pretrained models (Fu et al., 2023a). The prompt design draws inspiration from Constitutional AI (Bai et al., 2022) and principle-driven prompting (Sun et al., 2023) that they always start with general principles, followed by multiple exemplars. In the evaluation process, we use a temperature of 0.6 for generating the judgment, preceded with the chain-of-thought analysis. Each model is evaluated 8 times, and the average accuracy is reported. The few-shot exemplars always end with the pattern "Judgment: X.", where X is either correct or incorrect. We search for this pattern in the model output and extract X. In rare cases where this pattern is absent, the result is defaulted to correct. F.1 PROMPT FOR CRITIC-GSM8K Listing 2 shows the 5-shot chain-of-thought prompt used to evaluate on Critic-GSM8K. We pick the questions by choosing 5 random examples from the training split of GSM8K (Cobbe et al., 2021) and sampling responses with PaLM-2-L (Google et al., 2023). We manually select the responses with appropriate quality. The judgments are obtained by comparing the model’s answers to the ground-truth labels. F.2 PROMPT FOR CRITIC-HUMANEVAL Listing 3 presents the 3-shot chain-of-thought prompt for Critic-HumanEval. Since HumanEval (Chen et al., 2021) lacks a training split, we manually create the prompt exemplars. F.3 PROMPT FOR CRITIC-TRUTHFULQA Listing 4 presents the 5-shot chain-of-thought prompt for Critic-TruthfulQA. Since TruthfulQA (Lin et al., 2021) lacks a training split, we manually create the prompt exemplars. 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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The Prompt Patterns That Decide If an AI Is “Correct” or “Wrong”

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