Summary of Argumentation Computation with Large Language Models : a Benchmark Study, by Zhaoqun Li et al.
Argumentation Computation with Large Language Models : A Benchmark Study
by Zhaoqun Li, Xiaotong Fang, Chen Chen, Mengze Li, Beishui Liao
First submitted to arxiv on: 21 Dec 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 This paper investigates the capabilities of large language models (LLMs) in determining the extensions of various abstract argumentation semantics. The researchers develop a benchmark comprising diverse abstract argumentation frameworks and curate detailed explanations of algorithms for computing extensions. They fine-tune LLMs on this benchmark, focusing on two fundamental extension-solving tasks. While LLMs trained solely with question-answer pairs struggle to accurately compute semantics, those trained with process explanations show superior generalization accuracy. The approach also leverages the self-explanation capabilities of LLMs to provide detailed illustrations, mitigating the lack of transparency typically associated with neural networks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In this paper, scientists study how large language models can be used for reasoning and argumentation. They create a special dataset and algorithms to help these models learn how to understand different types of abstract arguments. The researchers find that when they teach these models to explain their thought processes, they become much better at solving problems and providing clear explanations. |
Keywords
» Artificial intelligence » Generalization » Semantics
