我们争论以达成一致：面向旅游业的性格驱动的基于论辩的协商对话系统
We Argue to Agree: Towards Personality-Driven Argumentation-Based Negotiation Dialogue Systems for Tourism
AI总结：
本文提出性格驱动的基于论辩的协商对话生成（PAN-DG）任务，并构建了面向旅游业的数据集PACT，通过微调LLMs有效生成个性化理性回复，提升了协商对话系统的个性化和推理能力。
摘要：
将论辩机制集成到协商对话系统中，可通过论据和批评的交换改善冲突解决。此外，融入性格属性可通过使交互与个人偏好和风格相一致来增强适应性。为推进协商对话系统中的这些能力，我们提出了一项新颖的性格驱动的基于论辩的协商对话生成（PAN-DG）任务。为支持该任务，我们引入了PACT，一个面向旅游业部门的性格驱动的基于论辩的协商对话数据集。该数据集使用大型语言模型（LLMs）生成，包含三种不同的性格画像，即论辩画像、偏好画像和购买风格画像，以模拟涉及不同性格的多种协商场景。全面的自动和人工评估表明，该数据集包含高质量的对话。此外，我们在预训练和微调的LLMs之间针对PAN-DG任务进行了比较实验。多维度评估表明，微调的LLMs能在协商过程中有效生成性格驱动的理性回复。这凸显了PACT在增强协商对话系统中的个性化和推理能力方面的有效性，从而为该领域的未来研究奠定了基础。
英文摘要：
Integrating argumentation mechanisms into negotiation dialogue systems improves conflict resolution through exchanges of arguments and critiques. Moreover, incorporating personality attributes enhances adaptability by aligning interactions with individuals' preferences and styles. To advance these capabilities in negotiation dialogue systems, we propose a novel Personality-driven Argumentation-based Negotiation Dialogue Generation (PAN-DG) task. To support this task, we introduce PACT, a dataset of Personality-driven Argumentation-based negotiation Conversations for Tourism sector. This dataset, generated using Large Language Models (LLMs), features three distinct personality profiles, viz. Argumentation Profile, Preference Profile, and Buying Style Profile to simulate a variety of negotiation scenarios involving diverse personalities. Thorough automatic and manual evaluations indicate that the dataset comprises high-quality dialogues. Further, we conduct comparative experiments between pre-trained and fine-tuned LLMs for the PAN-DG task. Multi-dimensional evaluation demonstrates that the fine-tuned LLMs effectively generate personality-driven rational responses during negotiations. This underscores the effectiveness of PACT in enhancing personalization and reasoning capabilities in negotiation dialogue systems, thereby establishing a foundation for future research in this domain.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
