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

• URL: http://arxiv.org/abs/2410.12509v1
• Date: Wed, 16 Oct 2024 12:36:23 GMT
• Title: Benchmarking Defeasible Reasoning with Large Language Models -- Initial Experiments and Future Directions
• Authors: Ilias Tachmazidis, Sotiris Batsakis, Grigoris Antoniou,
• Abstract summary: 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 Large Language Models. We conducted preliminary experiments on nonmonotonic rule-based reasoning using ChatGPT and compared it with reasoning patterns defined by defeasible logic.
• Score: 0.36868085124383626
• License:
• 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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