Summary of Benchmarking Defeasible Reasoning with Large Language Models — Initial Experiments and Future Directions, by Ilias Tachmazidis et al.
Benchmarking Defeasible Reasoning with Large Language Models – Initial Experiments and Future Directions
by Ilias Tachmazidis, Sotiris Batsakis, Grigoris Antoniou
First submitted to arxiv on: 16 Oct 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper proposes a benchmark to evaluate the capabilities of Large Language Models (LLMs) in nonmonotonic reasoning, which is crucial for understanding their strengths and limitations. The authors modified an existing benchmark for defeasible logic reasoners to translate defeasible rules into text suitable for LLMs, such as ChatGPT. Preliminary experiments compared the performance of LLMs with reasoning patterns defined by defeasible logic, providing insights into the models’ abilities in nonmonotonic rule-based reasoning. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us understand how well Large Language Models can reason about tricky situations where rules don’t always apply. To do this, they changed a test for special kind of logics called defeasible logic to work with language models like ChatGPT. They tested these models and found out how they compare to the original logic-based reasoning methods. |
