Persuasion Dynamics in LLMs: Investigating Robustness and Adaptability
  in Knowledge and Safety with DuET-PD
Authors: name: Bryan Chen Zhengyu Tan, name: Daniel Wai Kit Chin, name: Zhengyuan Liu, name: Nancy F. Chen, name: Roy Ka-Wei Lee

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.