# Persuasion Dynamics in LLMs: Investigating Robustness and Adaptability in Knowledge and Safety with DuET-PD

arXiv: https://arxiv.org/abs/2508.17450 · HTML: https://mlsanity.com/paper/2508.17450/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- Builds on 29 of its 48 references that are in the map.
- First posted on arXiv 24 Aug 2025.

## Builds on (most cited first, 29 of 29)

- [Direct Preference Optimization: Your Language Model is Secretly a Reward Model](https://mlsanity.com/paper/2305.18290/) (2023)
- [Retrieval-Augmented Generation for Large Language Models: A Survey](https://mlsanity.com/paper/2312.10997/) (2023)
- [MMLU-Pro: A More Robust and Challenging Multi-Task Language Understanding Benchmark](https://mlsanity.com/paper/2406.01574/) (2024)
- [LlamaFactory: Unified Efficient Fine-Tuning of 100+ Language Models](https://mlsanity.com/paper/2403.13372/) (2024)
- [Towards Understanding Sycophancy in Language Models](https://mlsanity.com/paper/2310.13548/) (2023)
- [How Johnny Can Persuade LLMs to Jailbreak Them: Rethinking Persuasion to Challenge AI Safety by Humanizing LLMs](https://mlsanity.com/paper/2401.06373/) (2024)
- [Can Generalist Foundation Models Outcompete Special-Purpose Tuning? Case Study in Medicine](https://mlsanity.com/paper/2311.16452/) (2023)
- [Large Language Models for Education: A Survey and Outlook](https://mlsanity.com/paper/2403.18105/) (2024)
- [X-Teaming: Multi-Turn Jailbreaks and Defenses with Adaptive Multi-Agents](https://mlsanity.com/paper/2504.13203/) (2025)
- [Language Models Learn to Mislead Humans via RLHF](https://mlsanity.com/paper/2409.12822/) (2024)
- [The Earth is Flat because...: Investigating LLMs' Belief towards Misinformation via Persuasive Conversation](https://mlsanity.com/paper/2312.09085/) (2023)
- [Sycophancy in Large Language Models: Causes and Mitigations](https://mlsanity.com/paper/2411.15287/) (2024)
- [Hidden Persuaders: LLMs' Political Leaning and Their Influence on Voters](https://mlsanity.com/paper/2410.24190/) (2024)
- [Tricking LLMs into Disobedience: Formalizing, Analyzing, and Detecting Jailbreaks](https://mlsanity.com/paper/2305.14965/) (2023)
- [Evaluating GPT-3 Generated Explanations for Hateful Content Moderation](https://mlsanity.com/paper/2305.17680/) (2023)
- [Persuasion with Large Language Models: A Survey of Empirical Evidence, Study Methodologies, and Ethical Implications](https://mlsanity.com/paper/2411.06837/) (2024)
- [Unmasking Implicit Bias: Evaluating Persona-Prompted LLM Responses in Power-Disparate Social Scenarios](https://mlsanity.com/paper/2503.01532/) (2025)
- [How susceptible are LLMs to Logical Fallacies?](https://mlsanity.com/paper/2308.09853/) (2023)
- [Teaching Models to Balance Resisting and Accepting Persuasion](https://mlsanity.com/paper/2410.14596/) (2024)
- [Exploiting Large Language Models (LLMs) through Deception Techniques and Persuasion Principles](https://mlsanity.com/paper/2311.14876/) (2023)
- [Reasoning Like a Doctor: Improving Medical Dialogue Systems via Diagnostic Reasoning Process Alignment](https://mlsanity.com/paper/2406.13934/) (2024)
- [Resolving Conflicting Evidence in Automated Fact-Checking: A Study on Retrieval-Augmented LLMs](https://mlsanity.com/paper/2505.17762/) (2025)
- [Is LLM an Overconfident Judge? Unveiling the Capabilities of LLMs in Detecting Offensive Language with Annotation Disagreement](https://mlsanity.com/paper/2502.06207/) (2025)
- [Persuasion at Play: Understanding Misinformation Dynamics in Demographic-Aware Human-LLM Interactions](https://mlsanity.com/paper/2503.02038/) (2025)
- [Moral Persuasion in Large Language Models: Evaluating Susceptibility and Ethical Alignment](https://mlsanity.com/paper/2411.11731/) (2024)
- [Examining the Influence of Political Bias on Large Language Model Performance in Stance Classification](https://mlsanity.com/paper/2407.17688/) (2024)
- [To Tell The Truth: Language of Deception and Language Models](https://mlsanity.com/paper/2311.07092/) (2023)
- [Can a large language model be a gaslighter?](https://mlsanity.com/paper/2410.09181/) (2024)
- [Sword and Shield: Uses and Strategies of LLMs in Navigating Disinformation](https://mlsanity.com/paper/2506.07211/) (2025)

## Built on by (most cited first, 13 of 13)

- [Accumulating Context Changes the Beliefs of Language Models](https://mlsanity.com/paper/2511.01805/) (2025)
- [When Correct Beliefs Collapse: Epistemic Resilience of LLMs under Clinical Pressure](https://mlsanity.com/paper/2605.23932/) (2026)
- [The Imperfect Learner: Incorporating Developmental Trajectories in Memory-based Student Simulation](https://mlsanity.com/paper/2511.05903/) (2025)
- [Persuasion Propagation: Does Persuasion Change AI Agent Behavior?](https://mlsanity.com/paper/2602.00851/) (2026)
- [AREG: Adversarial Resource Extraction Game for Evaluating Persuasion and Resistance in Large Language Models](https://mlsanity.com/paper/2602.16639/) (2026)
- [Right or Wrong, Models Comply: Directional Blindness in LLM Moral Judgment](https://mlsanity.com/paper/2606.14037/) (2026)
- [Assessing and Explaining the Persuadability of Large Language Models as Legal Decision Tools](https://mlsanity.com/paper/2604.26233/) (2026)
- [Psi-Bench: Evaluating Persona-Sensitive Influencing in Persuasive Dialogues](https://mlsanity.com/paper/2606.02754/) (2026)
- [Confident but Conflicted: Internal Uncertainty and Cognitive Dissonance Resolution in LLMs](https://mlsanity.com/paper/2606.22633/) (2026)
- [Bridging Talk and Thought: Understanding Dialogue Dynamics Across Collaborative Problem-Solving Contexts](https://mlsanity.com/paper/2606.27233/) (2026)
- [CC-Mediation: Evaluating Large Language Models for Cross-Cultural Conflict Mediation](https://mlsanity.com/paper/2609.04855/) (2026)
- [Do LLMs Trust the Accuser or the Accusation? Measuring Belief Shifts in Werewolf](https://mlsanity.com/paper/2609.12446/) (2026)
- [AfriSyCo: Measuring Assertive Framing, Verification, and Wording Sensitivity Around African-Language Content](https://mlsanity.com/paper/2609.17853/) (2026)

## Abstract (arXiv)

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.