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Large language models (LLMs) exhibit strikingly conflicting behaviors: they can appear steadfastly overconfident in their initial answers whilst at the same time being prone to excessive doubt when challenged. To investigate this apparent…

Robust verbal confidence generated by large language models (LLMs) is crucial for the deployment of LLMs to help ensure transparency, trust, and safety in many applications, including those involving human-AI interactions. In this paper, we…

Computation and Language · Computer Science 2025-12-19 Stephen Obadinma , Xiaodan Zhu

There is a growing literature on reasoning by large language models (LLMs), but the discussion on the uncertainty in their responses is still lacking. Our aim is to assess the extent of confidence that LLMs have in their answers and how it…

Computation and Language · Computer Science 2024-12-23 Yudi Pawitan , Chris Holmes

Large language models (LLMs) encapsulate vast amounts of knowledge but still remain vulnerable to external misinformation. Existing research mainly studied this susceptibility behavior in a single-turn setting. However, belief can change…

Computation and Language · Computer Science 2024-06-03 Rongwu Xu , Brian S. Lin , Shujian Yang , Tianqi Zhang , Weiyan Shi , Tianwei Zhang , Zhixuan Fang , Wei Xu , Han Qiu

Large Language Models (LLMs) are increasingly used in intelligent systems that perform reasoning, summarization, and code generation. Their ability to follow natural-language instructions, while powerful, also makes them vulnerable to a new…

Cryptography and Security · Computer Science 2025-11-13 Daniyal Ganiuly , Assel Smaiyl

Can LLMs accurately adjust their confidence when facing opposition? Building on previous studies measuring calibration on static fact-based question-answering tasks, we evaluate Large Language Models (LLMs) in a dynamic, adversarial debate…

Computation and Language · Computer Science 2025-06-10 Pradyumna Shyama Prasad , Minh Nhat Nguyen

Large Language Models (LLMs) are increasingly deployed in real-world applications where users engage in extended, mixed-topic conversations that depend on prior context. Yet, their reliability under realistic multi-turn interactions remains…

Computation and Language · Computer Science 2026-03-03 Jiyoon Myung

Large Language Models (LLMs) often produce explanations that do not faithfully reflect the factors driving their predictions. In healthcare settings, such unfaithfulness is especially problematic: explanations that omit salient clinical…

Computation and Language · Computer Science 2025-11-04 Teague McMillan , Gabriele Dominici , Martin Gjoreski , Marc Langheinrich

Large Language Models (LLMs) interact with millions of people worldwide in applications such as customer support, education and healthcare. However, their ability to produce deceptive outputs, whether intentionally or inadvertently, poses…

Computation and Language · Computer Science 2025-10-17 Marwa Abdulhai , Ryan Cheng , Aryansh Shrivastava , Natasha Jaques , Yarin Gal , Sergey Levine

Previous work has showcased the intriguing capabilities of Large Language Models (LLMs) in instruction-following and rhetorical fluency. However, systematic exploration of their dual capabilities to autonomously persuade and resist…

Computation and Language · Computer Science 2025-06-10 Tianjie Ju , Yujia Chen , Hao Fei , Mong-Li Lee , Wynne Hsu , Pengzhou Cheng , Zongru Wu , Zhuosheng Zhang , Gongshen Liu

Large Language Models (LLMs) are becoming increasingly persuasive, demonstrating the ability to personalize arguments in conversation with humans by leveraging their personal data. This may have serious impacts on the scale and…

Computation and Language · Computer Science 2025-01-30 Jasper Timm , Chetan Talele , Jacob Haimes

Large language models (LLMs) have become mainstream technology with their versatile use cases and impressive performance. Despite the countless out-of-the-box applications, LLMs are still not reliable. A lot of work is being done to improve…

Computation and Language · Computer Science 2023-06-13 Aisha Khatun , Daniel G. Brown

Large language models (LLMs) are capable of generating plausible explanations of how they arrived at an answer to a question. However, these explanations can misrepresent the model's "reasoning" process, i.e., they can be unfaithful. This,…

Computation and Language · Computer Science 2025-05-21 Katie Matton , Robert Osazuwa Ness , John Guttag , Emre Kıcıman

We are exposed to much information trying to influence us, such as teaser messages, debates, politically framed news, and propaganda - all of which use persuasive language. With the recent interest in Large Language Models (LLMs), we study…

Computation and Language · Computer Science 2025-02-24 Amalie Brogaard Pauli , Isabelle Augenstein , Ira Assent

Warning: This research studies AI persuasion and bias amplification that could be misused; all experiments are for safety evaluation. Large Language Models (LLMs) now generate convincing, human-like text and are widely used in content…

Computation and Language · Computer Science 2025-08-25 Saumya Roy

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…

Computation and Language · Computer Science 2025-09-10 Bryan Chen Zhengyu Tan , Daniel Wai Kit Chin , Zhengyuan Liu , Nancy F. Chen , Roy Ka-Wei Lee

Large Language Models (LLMs) demonstrate persuasive capabilities that rival human-level persuasion. While these capabilities can be used for social good, they also present risks of potential misuse. Beyond the concern of how LLMs persuade…

Computation and Language · Computer Science 2026-05-28 Nimet Beyza Bozdag , Shuhaib Mehri , Gokhan Tur , Dilek Hakkani-Tür

The rapid rise of Large Language Models (LLMs) has created new disruptive possibilities for persuasive communication, enabling fully-automated, personalized, and interactive content generation at an unprecedented scale. In this paper, we…

Computation and Language · Computer Science 2026-04-22 Sander Noels , Alexander Rogiers , Maarten Buyl , Tijl De Bie

Large language models (LLMs) inherit biases from their training data and alignment processes, influencing their responses in subtle ways. While many studies have examined these biases, little work has explored their robustness during…

Computation and Language · Computer Science 2024-11-06 Virgile Rennard , Christos Xypolopoulos , Michalis Vazirgiannis

Large language models (LLMs) are increasingly tasked with strategic decision-making under incomplete information, such as in negotiation and policymaking. While LLMs can excel at many such tasks, they also fail in ways that are poorly…

Computation and Language · Computer Science 2026-05-04 Jan Sobotka , Mustafa O. Karabag , Ufuk Topcu
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