用大模型根据性格特点定制辟谣内容，提升说服力。
Enhancing Debunking Effectiveness through LLM-based Personality Adaptation
- 用五大性格特质引导大模型生成个性化辟谣文本
- 开放性高者更易被说服，神经质者说服难度更高
- 多模型评估更可靠，适合想精准打击假新闻的团队
本研究提出一种新方法，通过基于人格特质（外向性、宜人性、尽责性、神经质、开放性）的输入提示大语言模型（LLMs），生成个性化的假新闻辟谣信息。该方法将通用辟谣内容转化为针对特定人格特征的定制版本。为评估效果，我们使用另一大模型作为自动化评估器，模拟对应人格特质，避免依赖昂贵的人工评测。结果表明，个性化辟谣信息普遍比通用版本更具说服力。开放性高的个体更易被说服，而神经质倾向者则表现出较低的可说服性。不同大模型间的评估差异也表明，采用多模型综合评估能提供更清晰的判断。本研究展示了利用大模型实现精准辟谣的可行性，同时也引发关于该技术伦理应用的重要思考。
原文摘要 · Abstract (English)
This study proposes a novel methodology for generating personalized fake news debunking messages by prompting Large Language Models (LLMs) with persona-based inputs aligned to the Big Five personality traits: Extraversion, Agreeableness, Conscientiousness, Neuroticism, and Openness. Our approach guides LLMs to transform generic debunking content into personalized versions tailored to specific personality profiles. To assess the effectiveness of these transformations, we employ a separate LLM as an automated evaluator simulating corresponding personality traits, thereby eliminating the need for costly human evaluation panels. Our results show that personalized messages are generally seen as more persuasive than generic ones. We also find that traits like Openness tend to increase persuadability, while Neuroticism can lower it. Differences between LLM evaluators suggest that using multiple models provides a clearer picture. Overall, this work demonstrates a practical way to create more targeted debunking messages exploiting LLMs, while also raising important ethical questions about how such technology might be used.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。