Gauging the inferential logic capabilities epitomizes a quintessential checkpoint for LLMs. As they continue to mirror human-like reasoning in various tasks, the question of their logic application lingers. The paper, Can LLMs Reason with Rules? Logic Scaffolding for Stress-Testing and Improving LLMs, presents a structured approach to testing and refining LLMs’ logical reasoning through the construction of ULogic, a comprehensive rule base.

Notable Discoveries:

• LLMs struggle with complex compositional and structural logic rules.
• Human performance remains unmatched in inferential rule applications, highlighting a knowledge gap for LLMs.
• Employing a distilled inference engine can improve commonsense reasoning tasks performance.

The paper is a harbinger for the future of logical reasoning in AI. By unveiling the weak spots in LLMs’ logical acumen, it throws down the gauntlet for the AI community, challenging us to devise methods to fortify LLM reasoning. This work stands as a beacon, guiding future explorations into realm where LLMs don’t just compute, but infer with unwavering logical clarity.