Summary of Can Formal Argumentative Reasoning Enhance Llms Performances?, by Federico Castagna et al.
Can formal argumentative reasoning enhance LLMs performances?
by Federico Castagna, Isabel Sassoon, Simon Parsons
First submitted to arxiv on: 16 May 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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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 novel approach to improve Large Language Models (LLMs) by incorporating computational argumentation semantics. This method captures agents’ interactions and information conflicts, enhancing LLMs’ reasoning and conversational abilities without retraining. The authors present the MQArgEng pipeline and conduct an exploratory study using the MT-Bench dataset. Results show a moderate performance gain in most topical categories, suggesting promise for further research. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper looks at how to make AI language models better by adding something called “computational argumentation”. This means understanding how different people interact with each other and resolve disagreements. The authors think this could help improve the language model’s ability to reason and have conversations without needing to be retrained. They created a system called MQArgEng and tested it using some data. So far, the results look promising. |
Keywords
» Artificial intelligence » Language model » Semantics
