LLM+AL: Bridging Large Language Models and Action Languages for Complex Reasoning about Actions

• URL: http://arxiv.org/abs/2501.00830v2
• Date: Tue, 04 Feb 2025 14:37:29 GMT
• Title: LLM+AL: Bridging Large Language Models and Action Languages for Complex Reasoning about Actions
• Authors: Adam Ishay, Joohyung Lee,
• Abstract summary: "LLM+AL" is a method that bridges the natural language understanding capabilities of LLMs with the symbolic reasoning strengths of action languages. We compare "LLM+AL" against state-of-the-art LLMs, including ChatGPT-4, Claude 3 Opus, Gemini Ultra 1.0, and o1-preview. Our findings indicate that, although all methods exhibit errors, LLM+AL, with relatively minimal human corrections, consistently leads to correct answers.
• Score: 7.575628120822444
• License:
• Abstract: Large Language Models (LLMs) have made significant strides in various intelligent tasks but still struggle with complex action reasoning tasks that require systematic search. To address this limitation, we propose a method that bridges the natural language understanding capabilities of LLMs with the symbolic reasoning strengths of action languages. Our approach, termed "LLM+AL," leverages the LLM's strengths in semantic parsing and commonsense knowledge generation alongside the action language's proficiency in automated reasoning based on encoded knowledge. We compare LLM+AL against state-of-the-art LLMs, including ChatGPT-4, Claude 3 Opus, Gemini Ultra 1.0, and o1-preview, using benchmarks for complex reasoning about actions. Our findings indicate that, although all methods exhibit errors, LLM+AL, with relatively minimal human corrections, consistently leads to correct answers, whereas standalone LLMs fail to improve even with human feedback. LLM+AL also contributes to automated generation of action languages.

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