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Deductive Verification with Natural Programs: Case Studies
by Cosmological thinking: time, space and universal causation September 8th, 2024
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We present examples of deductive verification using Natural Program formats, showcasing how ChatGPT identifies ungrounded information and logical errors, and highlighting cases where the model struggles with premise numbers and grounded values.
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
Motivation and Problem Formulation
Deductively Verifiable Chain-of-Thought Reasoning
Conclusion, Acknowledgements and References
A Deductive Verification with Vicuna Models
C More Details on Answer Extraction
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.
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