Summary of Enhancing Dialogue Generation in Werewolf Game Through Situation Analysis and Persuasion Strategies, by Zhiyang Qi and Michimasa Inaba
Enhancing Dialogue Generation in Werewolf Game Through Situation Analysis and Persuasion Strategies
by Zhiyang Qi, Michimasa Inaba
First submitted to arxiv on: 29 Aug 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 In this paper, researchers tackle the challenges of large language models (LLMs) in dialogue systems by employing the Werewolf Game as a testing ground. They introduce a LLM-based AI that uses situation analysis to aid response generation for each role, including the werewolf. The werewolf role also employs various persuasion strategies to persuade other players, showcasing the capabilities of LLMs in complex interactive environments. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper explores how large language models can be used in dialogue systems to generate more natural and fluent conversations. It focuses on the challenges of managing continuous dialogues, memory retention, and minimizing hallucinations in these systems. By employing the Werewolf Game, the researchers demonstrate how LLMs can be used to test their capabilities in complex interactive environments. |
