Summary

Large Language Models (LLMs) like GPT have showcased remarkable cognitive performances but falter when compared to human logical inferencing. A logic scaffolding framework called ULogic is proposed to tackle this issue by generating an inferential rule base encompassing both basic and complex rules across domains. GPT-series models tested over this rule base reveal gaps in sophisticated rule comprehension.

Highlights

• Presentation of ULogic: an expansive collection of inferential rules for LLM evaluation.
• Detailed analysis of LLMs’ limitations in understanding compositional and structurally complex logic rules.
• Development of a small-scale inference engine to improve rule generation and enhance reasoning tasks.
• Evaluation showing the engine’s proficiency in generating precise and complex conclusions and premises.

This work is pivotal as it not only identifies the shortcomings of current LLMs in logical reasoning but also provides a blueprint for augmenting their inferential capacities. These revelations could steer future research towards creating more nuanced and context-aware language models.

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