Authors:
(1) Zhan Ling, UC San Diego and equal contribution;
(2) Yunhao Fang, UC San Diego and equal contribution;
(3) Xuanlin Li, UC San Diego;
(4) Zhiao Huang, UC San Diego;
(5) Mingu Lee, Qualcomm AI Research and Qualcomm AI Research
(6) Roland Memisevic, Qualcomm AI Research;
(7) Hao Su, UC San Diego. Table of Links Abstract and Introduction Related work Motivation and Problem Formulation Deductively Verifiable Chain-of-Thought Reasoning Experiments Limitations Conclusion, Acknowledgements and References A Deductive Verification with Vicuna Models B More Discussion on Improvements of Deductive Verification Accuracy Versus Improvements on Final Answer Correctness C More Details on Answer Extraction D Prompts E More Deductive Verification Examples E More Deductive Verification Examples In this section, we present more deductive verification examples using our Natural Program-based approach on single reasoning steps. In Tab. 18, we demonstrate that the language model (ChatGPT) not only successfully identifies ungrounded information, but also identifies logical errors within the given solutions. In Tab. 19, we illustrate a case where the language model fails to detect ungrounded premise numbers, mistakenly assuming that these numbers can be derived from grounded ones. Lastly, in Tab. 20, we illustrate a case where the language model is sometimes unable to correctly identify grounded numbers. This paper is available on arxiv under CC BY 4.0 DEED license. Authors: (1) Zhan Ling, UC San Diego and equal contribution; (2) Yunhao Fang, UC San Diego and equal contribution; (3) Xuanlin Li, UC San Diego; (4) Zhiao Huang, UC San Diego; (5) Mingu Lee, Qualcomm AI Research and Qualcomm AI Research (6) Roland Memisevic, Qualcomm AI Research; (7) Hao Su, UC San Diego. Authors: Authors: (1) Zhan Ling, UC San Diego and equal contribution; (2) Yunhao Fang, UC San Diego and equal contribution; (3) Xuanlin Li, UC San Diego; (4) Zhiao Huang, UC San Diego; (5) Mingu Lee, Qualcomm AI Research and Qualcomm AI Research (6) Roland Memisevic, Qualcomm AI Research; (7) Hao Su, UC San Diego. Table of Links Abstract and Introduction Abstract and Introduction Related work Related work Motivation and Problem Formulation Motivation and Problem Formulation Deductively Verifiable Chain-of-Thought Reasoning Deductively Verifiable Chain-of-Thought Reasoning Experiments Experiments Limitations Limitations Conclusion, Acknowledgements and References Conclusion, Acknowledgements and References A Deductive Verification with Vicuna Models A Deductive Verification with Vicuna Models B More Discussion on Improvements of Deductive Verification Accuracy Versus Improvements on Final Answer Correctness B More Discussion on Improvements of Deductive Verification Accuracy Versus Improvements on Final Answer Correctness C More Details on Answer Extraction C More Details on Answer Extraction D Prompts D Prompts E More Deductive Verification Examples E More Deductive Verification Examples E More Deductive Verification Examples In this section, we present more deductive verification examples using our Natural Program-based approach on single reasoning steps. In Tab. 18, we demonstrate that the language model (ChatGPT) not only successfully identifies ungrounded information, but also identifies logical errors within the given solutions. In Tab. 19, we illustrate a case where the language model fails to detect ungrounded premise numbers, mistakenly assuming that these numbers can be derived from grounded ones. Lastly, in Tab. 20, we illustrate a case where the language model is sometimes unable to correctly identify grounded numbers. 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

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Deductive Verification with Natural Programs: Case Studies

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