Linear Reasoning vs. Proof by Cases: Obstacles for Large Language Models in FOL Problem Solving

Researchers introduced a new first-order logic dataset called PC-FOL to assess large language models' ability to handle case-based reasoning problems, which are more challenging than linear reasoning tasks. This work highlights significant performance gaps in LLMs when dealing with complex logical structures and underscores the need for improved training data and methods to enhance automated natural language mathematical proof generation.

Read the full article at arXiv cs.CL (NLP)

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