Enhancing Dialogue Generation in Werewolf Game Through Situation Analysis and Persuasion Strategies

• URL: http://arxiv.org/abs/2408.16586v2
• Date: Wed, 4 Sep 2024 02:24:08 GMT
• Title: Enhancing Dialogue Generation in Werewolf Game Through Situation Analysis and Persuasion Strategies
• Authors: Zhiyang Qi, Michimasa Inaba,
• Abstract summary: This paper introduces a LLM-based Werewolf Game AI, where each role is supported by situation analysis to aid response generation. Various persuasion strategies are employed to effectively persuade other players to align with its actions.
• Score: 1.7725414095035827
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Recent advancements in natural language processing, particularly with large language models (LLMs) like GPT-4, have significantly enhanced dialogue systems, enabling them to generate more natural and fluent conversations. Despite these improvements, challenges persist, such as managing continuous dialogues, memory retention, and minimizing hallucinations. The AIWolfDial2024 addresses these challenges by employing the Werewolf Game, an incomplete information game, to test the capabilities of LLMs in complex interactive environments. This paper introduces a LLM-based Werewolf Game AI, where each role is supported by situation analysis to aid response generation. Additionally, for the werewolf role, various persuasion strategies, including logical appeal, credibility appeal, and emotional appeal, are employed to effectively persuade other players to align with its actions.

Related papers

• What if Red Can Talk? Dynamic Dialogue Generation Using Large Language Models [0.0]
  We introduce a dialogue filler framework that utilizes large language models (LLMs) to generate dynamic and contextually appropriate character interactions. We test this framework within the environments of Final Fantasy VII Remake and Pokemon. This study aims to assist developers in crafting more nuanced filler dialogues, thereby enriching player immersion and enhancing the overall RPG experience.
  arXiv  Detail & Related papers  (2024-07-29T19:12:18Z)
• Werewolf Arena: A Case Study in LLM Evaluation via Social Deduction [3.350801757799469]
  Werewolf Arena is a framework for evaluating large language models (LLMs) In Werewolf Arena, LLMs compete against each other, navigating the game's complex dynamics of deception, deduction, and persuasion. We demonstrate Werewolf Arena's utility through an arena-style tournament featuring Gemini and GPT models.
  arXiv  Detail & Related papers  (2024-07-18T23:41:05Z)
• Learning to Discuss Strategically: A Case Study on One Night Ultimate Werewolf [28.57358844115881]
  As a variant of the famous communication game Werewolf, One Night Ultimate Werewolf (ONUW) requires players to develop strategic discussion policies. We propose an RL-instructed language agent framework, where a discussion policy trained by reinforcement learning (RL) is employed to determine appropriate discussion tactics to adopt.
  arXiv  Detail & Related papers  (2024-05-30T11:07:06Z)
• States as Strings as Strategies: Steering Language Models with Game-Theoretic Solvers [44.64118885012762]
  A suitable model of the players, strategies, and payoffs associated with linguistic interactions would enable existing game-theoretic algorithms to provide strategic solutions in the space of language. We present one possible binding from dialogue to game theory as well as generalizations of existing equilibrium finding algorithms to this setting.
  arXiv  Detail & Related papers  (2024-01-24T22:22:00Z)
• Plug-and-Play Policy Planner for Large Language Model Powered Dialogue Agents [121.46051697742608]
  We introduce a new dialogue policy planning paradigm to strategize dialogue problems with a tunable language model plug-in named PPDPP. Specifically, we develop a novel training framework to facilitate supervised fine-tuning over available human-annotated data. PPDPP consistently and substantially outperforms existing approaches on three different proactive dialogue applications.
  arXiv  Detail & Related papers  (2023-11-01T03:20:16Z)
• BotChat: Evaluating LLMs' Capabilities of Having Multi-Turn Dialogues [72.65163468440434]
  This report provides a preliminary evaluation of existing large language models for human-style multi-turn chatting. We prompt large language models (LLMs) to generate a full multi-turn dialogue based on the ChatSEED, utterance by utterance. We find GPT-4 can generate human-style multi-turn dialogues with impressive quality, significantly outperforms its counterparts.
  arXiv  Detail & Related papers  (2023-10-20T16:53:51Z)
• Self-Explanation Prompting Improves Dialogue Understanding in Large Language Models [52.24756457516834]
  We propose a novel "Self-Explanation" prompting strategy to enhance the comprehension abilities of Large Language Models (LLMs) This task-agnostic approach requires the model to analyze each dialogue utterance before task execution, thereby improving performance across various dialogue-centric tasks. Experimental results from six benchmark datasets confirm that our method consistently outperforms other zero-shot prompts and matches or exceeds the efficacy of few-shot prompts.
  arXiv  Detail & Related papers  (2023-09-22T15:41:34Z)
• Exploring Large Language Models for Communication Games: An Empirical Study on Werewolf [19.39740531672788]
  We propose a tuning-free framework to engage large language models in communication games. An empirical study on the representative and widely-studied communication game, Werewolf'', demonstrates that our framework can effectively play Werewolf game without tuning the parameters of the LLMs.
  arXiv  Detail & Related papers  (2023-09-09T01:56:40Z)
• Building Emotional Support Chatbots in the Era of LLMs [64.06811786616471]
  We introduce an innovative methodology that synthesizes human insights with the computational prowess of Large Language Models (LLMs) By utilizing the in-context learning potential of ChatGPT, we generate an ExTensible Emotional Support dialogue dataset, named ExTES. Following this, we deploy advanced tuning techniques on the LLaMA model, examining the impact of diverse training strategies, ultimately yielding an LLM meticulously optimized for emotional support interactions.
  arXiv  Detail & Related papers  (2023-08-17T10:49:18Z)
• Tachikuma: Understading Complex Interactions with Multi-Character and Novel Objects by Large Language Models [67.20964015591262]
  We introduce a benchmark named Tachikuma, comprising a Multiple character and novel Object based interaction Estimation task and a supporting dataset. The dataset captures log data from real-time communications during gameplay, providing diverse, grounded, and complex interactions for further explorations. We present a simple prompting baseline and evaluate its performance, demonstrating its effectiveness in enhancing interaction understanding.
  arXiv  Detail & Related papers  (2023-07-24T07:40:59Z)
• A Mixture-of-Expert Approach to RL-based Dialogue Management [56.08449336469477]
  We use reinforcement learning to develop a dialogue agent that avoids being short-sighted (outputting generic utterances) and maximizes overall user satisfaction. Most existing RL approaches to DM train the agent at the word-level, and thus, have to deal with aly complex action space even for a medium-size vocabulary. We develop a RL-based DM using a novel mixture of expert language model (MoE-LM) that consists of (i) a LM capable of learning diverse semantics for conversation histories, (ii) a number of specialized LMs (or experts) capable of generating utterances corresponding to a
  arXiv  Detail & Related papers  (2022-05-31T19:00:41Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.