Persuasion in Language Models

Follow this topic Ask about this topic

Momentum

7 papers in the last four weeks, up 75% on the four weeks before. 0.1% of all new papers.

Jul 13Week of Sep 28

Latest papers 34

All topics

LLM Persuasion Is in the Eye of the Evaluation
Oct 7, 2026Kamile Dementaviciute, Julija Vaitonyte, Tijl De BiePersuasion in Language Models LLM Evaluation
Ghent University · Tilburg University, Department of Computational Cognitive Science · ISM University of Management and Economics
Successive Training Stages and Large Language Model Persuasion: Effects of Misalignment, Supervised Fine-Tuning, and Preference Optimization
Oct 7, 2026Antony Dalmiere, Pascal Marchand, Guillaume Auriol +1Persuasion in Language Models LLM Post-Training
LAAS-TRUST · LAAS-TRUST, INSA Toulouse · LAAS-TSF, LAAS
Persona and Persuasive Framing in AI Voice Agents: A $2\times2$ Field Experiment with Children
Sep 30, 2026Thilo Tamme, David Steck, Anton HantelHuman-AI Interaction Framing Effects in Language Models
Technical University of Munich · Massachusetts Institute of Technology
AI Agents are Vulnerable to Radicalization
Sep 29, 2026Ozgur Can Seckin, Shalmoli Ghosh, Alessandro Flammini +3Multi-Agent LLM Systems Belief Updating in LLMs
Observatory on Social Media, Indiana University Bloomington · Emerging Media Studies Division, College of Communication, Boston University
Persuaded, Not Informed: Incentive-Misaligned Witnesses Defeat In-Context Grounding
Sep 23, 2026Rahul BalakaviKnowledge Conflicts in Language Models Agent Evaluation
AmpUp Research
Benchmarking Argumentative Behaviour of LLMs: A Study of Defences Against Character Attacks
Sep 23, 2026Ewelina Gajewska, Katarzyna Budzynska, Jaroslaw ChudziakMulti-Agent Debate Computational Argumentation
Warsaw University of Technology
Conduct Under Pressure: What Sixty Language Models Do When a User Pushes
Sep 21, 2026Tapan ParikhLanguage Model Safety Evaluation Persuasion in Language Models
Cornell Tech
Benchmarking Factual Robustness of LLMs via Multi-conversation Persuasion
Sep 15, 2026Zhuoang CaiAdversarial Attacks on LLMs LLM Safety Evaluation
The Hong Kong University of Science and Technology
When Does Defendant Statement Matter? A Study of Bias and Persuasion in LLM-Simulated Jurors
Sep 9, 2026Cho-Ying WuLegal Judgment Prediction Social Bias in Language Models
Bosch AI Research
Door-in-the-Face Requests and Refusal Behaviour in Large Language Models
Sep 2, 2026Til JordanPersuasion in Language Models LLM Refusal Behavior
Do LLMs Change Their Minds Like Humans? Diagnosing Human--LLM Divergence in Single-Turn Persuasion Judgments
Aug 30, 2026Lin Chen, Yitong Chen, Yong LiLLM Evaluation Belief Updating in LLMs
1Network Science Institute, Northeastern University · Department of Physics, Northeastern University · Department of Electronic Engineering, BNRist, Tsinghua University
Evaluating the Capabilities of LLMs for Persuasive Dialogue
Aug 30, 2026Jordan Robinson, Angus R. Williams, Katie Atkinson +1LLM Evaluation Multi-Agent Debate
University of Liverpool · 2The Alan Turing Institute · University of Leeds
Learning to Persuade Exposes How Easily LLMs Abandon Correct Beliefs
Aug 12, 2026Nimet Beyza Bozdag, Emre Can Acikgoz, Gokhan Tur +1Deception in Language Models Adversarial Attacks on LLMs
University of Illinois Urbana-Champaign
Persuasive and Compliant Tendencies Predict Group Decision-Making in Humans and Language Models
Aug 8, 2026Wenwen He, Wenke Huang, Wei Yang Bryan Lim +1Multi-Agent LLM Systems Personality Modeling in Language Models
College of Computing and Data Science, Nanyang Technological University, Singapore
Evaluating VLMs on Multimodal Aristotelian Persuasion Tasks
Aug 2, 2026Khondoker Ittehadul IslamVLM Evaluation Vision-Language Models
Saarland University · Department of Language Science & Technology
It's Not What You Say, It's How You Say It: Evaluating LLM Responses to Expressions of Belief
Jul 20, 2026Kevin Du, Clara Kümpel, Michelle Wastl +1LLM Evaluation Knowledge Conflicts in Language Models
ETH Zürich · University of Zurich · UC San Diego
Persuasion Attacks Can Decrease Effectiveness of CoT Monitoring
Jul 9, 2026Jennifer Za, Julija Bainiaksina, Nikita Ostrovsky +2Language Model Safety Evaluation Adversarial Attacks on LLMs
LASR Labs · University College London · Google DeepMind
Theory of Mind and Persuasion Beyond Conversation: Assessing the Capacity of LLMs to Induce Belief States via Planning and Action
Jun 30, 2026Ben Slater, Matteo G. Mecattaf, Lucy G. Cheke +2Theory of Mind LLM Agent Evaluation
Leverhulme Centre for the Future of Intelligence Department of Psychology University of Cambridge, UK · Prolific · Google, Paradigms of Intelligence Team
Confident but Conflicted: Internal Uncertainty and Cognitive Dissonance Resolution in LLMs
Jun 21, 2026Weihong Qi, Kristina LermanKnowledge Conflicts in Language Models Belief Updating in LLMs
Indiana University Bloomington
Using Cognitive Models to Improve Language Model Simulation of Human Persuasion Games
Jun 16, 2026Zirui Cheng, Zeyu Shen, Thomas L. Griffiths +1RL for Language Models Cognitive Modeling
1Princeton University
AI systems out-persuade expert humans
Jun 15, 2026Kobi Hackenburg, Caroline Wagner, Luke Hewitt +5Human-AI Interaction Persuasion in Language Models
University of Oxford, Oxford, OX2 6GG, UK · 2UK AI Security Institute, London, UK · 3Stanford University, Stanford, CA 94305, USA +1
"I understand your perspective": LLM Persuasion and Sycophancy through the Lens of Communicative Action Theory
Jun 6, 2026Esra Dönmez, Agnieszka FalenskaLLM Sycophancy Computational Argumentation
Institute for Natural Language Processing, University of Stuttgart · Interchange Forum for Reflecting on Intelligent Systems, University of Stuttgart
A Model of Multi-turn Human Persuadability Using Probabilistic Belief Tracing
Jun 3, 2026Jared Moore, Noah Goodman, Nick Haber +1Opinion Dynamics Human-AI Interaction
Stanford University · University of Washington
$Ψ$ -Bench: Evaluating Persona-Sensitive Influencing in Persuasive Dialogues
Jun 1, 2026Peixuan Han, Hongyi Du, Jiayu Liu +3LLM Personalization LLM Agent Evaluation
University of Illinois Urbana-Champaign
MA $^{2}$ P: A Meta-Cognitive Autonomous Intelligent Agents Framework for Complex Persuasion
May 18, 2026Dingyi Zhang, Ziqing Zhuang, Linhai Zhang +2Multi-Agent LLM Systems Persuasion in Language Models
School of Computer Science and Engineering, Key Laboratory of Computer Network and Information Integration, Ministry of Education, Southeast University, China · Department of Informatics, King’s College London
LLM-Based Persuasion Enables Guardrail Override in Frontier LLMs
May 13, 2026Rodrigo Nogueira, Thales Sales Almeida, Giovana Kerche Bonás +7Adversarial Prompt Generation LLM Guardrails
Maritaca AI · JusBrasil
How LLMs Are Persuaded: A Few Attention Heads, Rerouted
May 10, 2026Xiangkun Sun, Lingkai Kong, Aoqi Zhang +2Self-Attention LLM Interpretability
Northeastern University · Harvard University · Tsinghua University +1
Spontaneous Persuasion: An Audit of Model Persuasiveness in Everyday Conversations
Apr 23, 2026Nalin Poungpeth, Nicholas Clark, Tanu MitraLLM Auditing Human-AI Interaction
Department of Cognitive Science Northwestern University Evanston, IL 60201, USA · Information School University of Washington Seattle, WA 98195 USA
Measuring Opinion Bias and Sycophancy via LLM-based Persuasion
Apr 23, 2026Rodrigo Nogueira, Giovana Kerche Bonás, Thales Sales Almeida +7LLM Sycophancy LLM Auditing
Maritaca AI · JusBrasil
Persuasion Propagation: Does Persuasion Change AI Agent Behavior?
Jan 31, 2026Hyejun Jeong, Amir Houmansadr, Shlomo Zilberstein +1Persuasion in Language Models AI Agent Evaluation
University of Massachusetts Amherst

Oct 7, 2026, cs.CLJ/K move · Enter open · S save

LLM Persuasion Is in the Eye of the Evaluation

Kamile Dementaviciute, Julija Vaitonyte, Tijl De Bie

Ghent University · Tilburg University, Department of Computational Cognitive Science · ISM University of Management and Economics

Persuasion in Language Models LLM Evaluation Automated Evaluation

Open paper AskPDF arXiv

Large language models (LLMs) have already been shown to match or exceed human experts in persuasion. While their persuasive capabilities hold promise for beneficial uses such as education and health communication, they can also be used to manipulate and misinform, making their evaluation a growing priority for developers and regulators. That evaluation, however, remains fragmented: studies differ in what they treat as persuasion, and broad claims often rest on narrow, situation-specific assessments. Automated methods, often modelled on human studies, offer a way to compare such assessments directly, as they can be run on the same models at scale and can include high-risk forms of persuasion that would be difficult or unethical to test on people. In this study, we adapt nine published automated methods to a shared setup, run them on the same fifteen LLMs, and ask whether their rankings agree and why. We find that the methods agree only weakly (mean Spearman

ρ= 0.25

). Our analyses point to two contributing factors. Models that refuse some tasks but not others, directly or indirectly, lower agreement by about a quarter, and these refusals fall mostly on manipulation tasks. General capability also plays a part: most rational persuasion (non-manipulative) methods track it, whereas most manipulation methods do not. Together, these findings suggest that agreement depends more on the task a method sets than on how it scores persuasion, although this pattern is only indicative given the eight methods available for analysis. More broadly, our results suggest that persuasion scores combine a model's ability to persuade with its willingness to do so. A single score is therefore informative about its own setting, but says little about a model's persuasiveness across tasks.

Momentum

Related topics

Latest papers 34