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Proof of Thought : Neurosymbolic Program Synthesis allows Robust and Interpretable Reasoning

Debargha Ganguly, Srinivasan Iyengar, Vipin Chaudhary, Shivkumar Kalyanaraman

TL;DR

Proof of Thought is introduced, a framework that enhances the reliability and transparency of LLM outputs and sets a foundation for human-in-the-loop oversight in high-stakes domains by employing a custom interpreter to convert LLM outputs into First Order Logic constructs for theorem prover scrutiny.

Abstract

Large Language Models (LLMs) have revolutionized natural language processing, yet they struggle with inconsistent reasoning, particularly in novel domains and complex logical sequences. This research introduces Proof of Thought, a framework that enhances the reliability and transparency of LLM outputs. Our approach bridges LLM-generated ideas with formal logic verification, employing a custom interpreter to convert LLM outputs into First Order Logic constructs for theorem prover scrutiny. Central to our method is an intermediary JSON-based Domain-Specific Language, which by design balances precise logical structures with intuitive human concepts. This hybrid representation enables both rigorous validation and accessible human comprehension of LLM reasoning processes. Key contributions include a robust type system with sort management for enhanced logical integrity, explicit representation of rules for clear distinction between factual and inferential knowledge, and a flexible architecture that allows for easy extension to various domain-specific applications. We demonstrate Proof of Thought's effectiveness through benchmarking on StrategyQA and a novel multimodal reasoning task, showing improved performance in open-ended scenarios. By providing verifiable and interpretable results, our technique addresses critical needs for AI system accountability and sets a foundation for human-in-the-loop oversight in high-stakes domains.

Proof of Thought : Neurosymbolic Program Synthesis allows Robust and Interpretable Reasoning

TL;DR

Proof of Thought is introduced, a framework that enhances the reliability and transparency of LLM outputs and sets a foundation for human-in-the-loop oversight in high-stakes domains by employing a custom interpreter to convert LLM outputs into First Order Logic constructs for theorem prover scrutiny.

Abstract

Large Language Models (LLMs) have revolutionized natural language processing, yet they struggle with inconsistent reasoning, particularly in novel domains and complex logical sequences. This research introduces Proof of Thought, a framework that enhances the reliability and transparency of LLM outputs. Our approach bridges LLM-generated ideas with formal logic verification, employing a custom interpreter to convert LLM outputs into First Order Logic constructs for theorem prover scrutiny. Central to our method is an intermediary JSON-based Domain-Specific Language, which by design balances precise logical structures with intuitive human concepts. This hybrid representation enables both rigorous validation and accessible human comprehension of LLM reasoning processes. Key contributions include a robust type system with sort management for enhanced logical integrity, explicit representation of rules for clear distinction between factual and inferential knowledge, and a flexible architecture that allows for easy extension to various domain-specific applications. We demonstrate Proof of Thought's effectiveness through benchmarking on StrategyQA and a novel multimodal reasoning task, showing improved performance in open-ended scenarios. By providing verifiable and interpretable results, our technique addresses critical needs for AI system accountability and sets a foundation for human-in-the-loop oversight in high-stakes domains.
Paper Structure (26 sections, 1 equation, 6 figures)

This paper contains 26 sections, 1 equation, 6 figures.

Table of Contents

  1. Introduction
  2. Related Work
  3. Proof of Thought: A Neurosymbolic Reasoning Framework
  4. Background Concepts & Notation from LLM Reasoning Literature
  5. Framework Overview
  6. Design of the JSON-Based Domain-Specific Language (DSL)
  7. Design of the Interpreter's Facilities and Capabilities
  8. Results
  9. StrategyQA - Complex Natural Language Reasoning
  10. Multimodal Reddit-OSHA Benchmark
  11. Discussion and Future work
  12. Conclusion
  13. Appendix
  14. StrategyQA Results
  15. Multimodal Reddit-OSHA Benchmark
  16. ...and 11 more sections

Figures (6)

  • Figure 1: Architecture of the Proof of Thought (PoT) framework, illustrating the integration of natural language reasoning with formal logical verification.
  • Figure 2: Example DSL program components of the Proof of Thought (PoT) framework for a dummy task assignment verification and optimization problem. The figure displays the JSON-based Domain-Specific Language (DSL) structure, including sort definitions, variables, functions, constants, knowledge base, rules, verifications, and optimization constraints for a workforce management scenario.
  • Figure 3: Performance analysis of the Proof of Thought (PoT) framework on the StrategyQA dataset. The includes four visualizations: (1) a stacked bar chart showing questions answered by attempt, (2) a pie chart displaying the final question status, (3) a confusion matrix for predicted vs. true labels, and (4) a bar chart of various performance metrics including accuracy, precision, recall, F1-score, specificity, and false positive rate.
  • Figure 4: Sample Images from the Multimodal Reddit-OSHA Benchmark
  • Figure 5: Performance analysis of the Proof of Thought (PoT) framework on the Multimodal Reddit-OSHA Benchmark dataset with a stacked bar chart showing questions answered by attempt.
  • ...and 1 more figures