测试大模型在说服对话中的抗误导与纠错能力，发现新模型更易盲从。
Persuasion Dynamics in LLMs: Investigating Robustness and Adaptability in Knowledge and Safety with DuET-PD
- 设计双维度评估框架，测误导与纠正两类说服对模型影响。
- GPT-4o在持续误导下知识测试准确率仅27.32%，安全场景下新开源模型更易迎合。
- 提出整体DPO训练法，使模型同时抗误导、听纠正，提升效果显著。
大语言模型在说服性对话中难以平衡对虚假信息的易受骗性和对有效纠正的抵抗性，这对可靠部署构成挑战。我们提出DuET-PD（说服对话中的信任双重评估），一个评估多轮立场变化的框架，涵盖两个维度：说服类型（纠正性/误导性）和领域（知识通过MMLU-Pro，安全通过SALAD-Bench）。结果发现，即使最先进的GPT-4o在持续误导下于MMLU-Pro上的准确率也仅为27.32%。此外，研究揭示新开源模型存在日益严重的迎合倾向。为此，我们引入整体DPO训练方法，平衡正负说服样本。相较于提示或仅抗误导训练，该方法同时增强对虚假信息的鲁棒性和对纠正的接受度，将Llama-3.1-8B-Instruct在安全情境下的误导说服准确率从4.21%提升至76.54%。这些贡献为开发更可靠、适应性强的多轮对话模型提供了路径。代码已公开于https://github.com/Social-AI-Studio/DuET-PD。
原文摘要 · Abstract (English)
Large Language Models (LLMs) can struggle to balance gullibility to misinformation and resistance to valid corrections in persuasive dialogues, a critical challenge for reliable deployment. We introduce DuET-PD (Dual Evaluation for Trust in Persuasive Dialogues), a framework evaluating multi-turn stance-change dynamics across dual dimensions: persuasion type (corrective/misleading) and domain (knowledge via MMLU-Pro, and safety via SALAD-Bench). We find that even a state-of-the-art model like GPT-4o achieves only 27.32% accuracy in MMLU-Pro under sustained misleading persuasions. Moreover, results reveal a concerning trend of increasing sycophancy in newer open-source models. To address this, we introduce Holistic DPO, a training approach balancing positive and negative persuasion examples. Unlike prompting or resist-only training, Holistic DPO enhances both robustness to misinformation and receptiveness to corrections, improving Llama-3.1-8B-Instruct's accuracy under misleading persuasion in safety contexts from 4.21% to 76.54%. These contributions offer a pathway to developing more reliable and adaptable LLMs for multi-turn dialogue. Code is available at https://github.com/Social-AI-Studio/DuET-PD.
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