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To Be or Not to Be Persuaded: Persuasion in Large Language Models
Published:
Authors: Nimet Beyza Bozdag, Shuhaib Mehri, Gokhan Tur, Dilek Hakkani-Tür Date: 03/23/2026
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publications
Language Specific Knowledge: Do Models Know Better in X than in English?
Published in arXiv, 2025
This paper introduces Language Specific Knowledge (LSK), exploring how language models can exhibit better reasoning in languages other than English, particularly in culturally specific contexts.
Recommended citation: Ishika Agarwal, Nimet Beyza Bozdag, Dilek Hakkani-Tür. 2025. Language Specific Knowledge: Do Models Know Better in X than in English?. arXiv:2505.14990
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Persuade Me if You Can: A Framework for Evaluating Persuasion Effectiveness and Susceptibility Among Large Language Models
Published in Workshop on Multi-Turn Interactions in Large Language Models @ NeurIPS 2025, 2025
This paper introduces PMIYC, an automated framework for evaluating persuasion effectiveness and susceptibility in large language models through multi-agent interactions.
Recommended citation: Nimet Beyza Bozdag, Shuhaib Mehri, Gokhan Tur, and Dilek Hakkani-Tür. 2025. Persuade Me if You Can: A Framework for Evaluating Persuasion Effectiveness and Susceptibility Among Large Language Models. Workshop on Multi-Turn Interactions in Large Language Models @ NeurIPS 2025
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Must Read: A Comprehensive Survey of Computational Persuasion
Published in ACM Computing Surveys, 2026
This paper is a systematic survey of computational persuasion, focusing on AI as a persuader, persuadee, and judge.
Recommended citation: Nimet Beyza Bozdag, Shuhaib Mehri, Xiaocheng Yang, Hyeonjeong Ha, Zirui Cheng, Esin Durmus, Jiaxuan You, Heng Ji, Gokhan Tur, Dilek Hakkani-Tür. 2026. Must Read: A Comprehensive Survey of Computational Persuasion. ACM Computing Surveys.
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talks
Introducing PMIYC: A Framework for Evaluating Persuasion Effectiveness and Susceptibility
Published:
In this presentation we introduce PMIYC, an automated framework for evaluating persuasion effectiveness and susceptibility in large language models through multi-agent interactions. We discuss how Persuader agents engage in multi-turn conversations with Persuadee agents, allowing us to measure LLMs’ persuasive effectiveness and their susceptibility to persuasion. We present our findings on various models, showing significant differences in persuasive capabilities and resistance to misinformation. This work contributes to understanding the dynamics of persuasion in AI systems and aims to enhance the safety and ethical alignment of language models.
teaching
Undergraduate Teaching Assistant
Undergraduate CS Courses, University of Arizona, Department of Computer Science, 2024
As an Undergraduate Teaching Assistant (UGTA) at the University of Arizona, I have had the opportunity to assist in various computer science courses.
Graduate Teaching Assistant
CS 447: Natural Language Processing, University of Illinois at Urbana-Champaign, Department of Computer Science, 2026
As a Graduate Teaching Assistant (GTA) at the University of Illinois at Urbana-Champaign, I have had the opportunity to assist in the CS 447: Natural Language Processing course.