用多模型对话自动构建高质量说服性数据集
Communication is All You Need: Persuasion Dataset Construction via Multi-LLM Communication
- 多大模型协作生成对话，减少人工干预
- 生成内容自然度高，涵盖敏感话题且策略性强
- 适合研究说服机制或需要海量对话数据的学者
大型语言模型在生成说服性对话方面已展现出能力，但其输出的流畅性和复杂性仍存疑。本文提出一种多大模型通信框架，旨在自动提升说服性数据的生成质量。该框架可高效产出高质量、多样化的语言内容，几乎无需人工介入。大量评估表明，生成数据在自然度、语言多样性及说服策略运用上表现优异，即便在涉及社会禁忌的复杂场景中也具备良好适应性。该框架还表现出跨新情境的泛化能力。结果表明，该框架有望显著推动计算社会科学领域对说服性沟通的研究。
原文摘要 · Abstract (English)
Large Language Models (LLMs) have shown proficiency in generating persuasive dialogue, yet concerns about the fluency and sophistication of their outputs persist. This paper presents a multi-LLM communication framework designed to enhance the generation of persuasive data automatically. This framework facilitates the efficient production of high-quality, diverse linguistic content with minimal human oversight. Through extensive evaluations, we demonstrate that the generated data excels in naturalness, linguistic diversity, and the strategic use of persuasion, even in complex scenarios involving social taboos. The framework also proves adept at generalizing across novel contexts. Our results highlight the framework's potential to significantly advance research in both computational and social science domains concerning persuasive communication.
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