Benchmarking Defeasible Reasoning with Large Language Models -- Initial Experiments and Future Directions

Ilias Tachmazidis; Sotiris Batsakis; Grigoris Antoniou

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arXiv · 2410.12509

Benchmarking Defeasible Reasoning with Large Language Models -- Initial Experiments and Future Directions

Ilias Tachmazidis·Sotiris Batsakis·Grigoris Antoniou

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Abstract

Large Language Models (LLMs) have gained prominence in the AI landscape due to their exceptional performance. Thus, it is essential to gain a better understanding of their capabilities and limitations, among others in terms of nonmonotonic reasoning. This paper proposes a benchmark that corresponds to various defeasible rule-based reasoning patterns. We modified an existing benchmark for defeasible logic reasoners by translating defeasible rules into text suitable for LLMs. We conducted preliminary experiments on nonmonotonic rule-based reasoning using ChatGPT and compared it with reasoning patterns defined by defeasible logic.

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Ilias Tachmazidis, Sotiris Batsakis, Grigoris Antoniou. 2024-10-16. Benchmarking Defeasible Reasoning with Large Language Models -- Initial Experiments and Future Directions. https://arxiv.org/abs/2410.12509

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