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As large language models are deployed as autonomous agents, their capacity for strategic deception raises core questions for coordination, reliability, and safety in multi-goal, multi-agent systems. We study deception and communication in…

多智能体系统 · 计算机科学 2026-03-30 Maria Milkowski , Tim Weninger

Strategic social deduction games serve as valuable testbeds for evaluating the understanding and inference skills of language models, offering crucial insights into social science, artificial intelligence, and strategic gaming. This paper…

计算与语言 · 计算机科学 2024-07-25 Yizhou Chi , Lingjun Mao , Zineng Tang

Large Language Models (LLMs) have emerged as formidable instruments capable of comprehending and producing human-like text. This paper explores the potential of LLMs, to shape user perspectives and subsequently influence their decisions on…

人工智能 · 计算机科学 2024-09-04 Ganesh Prasath Ramani , Shirish Karande , Santhosh V , Yash Bhatia

Prior studies on deception in language-based AI agents typically assess whether the agent produces a false statement about a topic, or makes a binary choice prompted by a goal, rather than allowing open-ended deceptive behavior to emerge in…

人工智能 · 计算机科学 2026-02-11 Satvik Golechha , Adrià Garriga-Alonso

Large Language Models (LLMs) can generate content that is as persuasive as human-written text and appear capable of selectively producing deceptive outputs. These capabilities raise concerns about potential misuse and unintended…

计算与语言 · 计算机科学 2024-12-24 Cameron R. Jones , Benjamin K. Bergen

Large Language Models (LLMs) have shown remarkable promise in communicating with humans. Their potential use as artificial partners with humans in sociological experiments involving conversation is an exciting prospect. But how viable is…

人工智能 · 计算机科学 2025-02-04 James Flamino , Mohammed Shahid Modi , Boleslaw K. Szymanski , Brendan Cross , Colton Mikolajczyk

Ideal or real - that is the question.In this work, we explore whether principles from game theory can be effectively applied to the evaluation of large language models (LLMs). This inquiry is motivated by the growing inadequacy of…

计算与语言 · 计算机科学 2026-04-07 Gao Yang , Yuhang Liu , Siyu Miao , Xinyue Liang , Zhengyang Liu , Heyan Huang

Deception and persuasion play a critical role in long-horizon dialogues between multiple parties, especially when the interests, goals, and motivations of the participants are not aligned. Such complex tasks pose challenges for current…

The emergence of Large Language Models (LLMs) has great potential to reshape the landscape of many social media platforms. While this can bring promising opportunities, it also raises many threats, such as biases and privacy concerns, and…

人机交互 · 计算机科学 2024-02-14 Kristina Radivojevic , Nicholas Clark , Paul Brenner

Large language models (LLMs) are increasingly deployed to support human decision-making. This use of LLMs has concerning implications, especially when their prescriptions affect the welfare of others. To gauge how LLMs make social…

计算机与社会 · 计算机科学 2026-01-16 Saptarshi Pal , Abhishek Mallela , Christian Hilbe , Lenz Pracher , Chiyu Wei , Feng Fu , Santiago Schnell , Martin A Nowak

Large language models (LLMs) have demonstrated strong persuasive capabilities comparable to those of humans, offering promising benefits while raising societal concerns. However, systematically evaluating the persuasive capabilities of LLMs…

人工智能 · 计算机科学 2026-03-10 Zirui Cheng , Jiaxuan You

Large language models (LLMs) are currently at the forefront of intertwining artificial intelligence (AI) systems with human communication and everyday life. Thus, aligning them with human values is of great importance. However, given the…

计算与语言 · 计算机科学 2024-06-06 Thilo Hagendorff

Large Language Models (LLMs) have shown impressive capabilities in complex tasks and interactive environments, yet their creativity remains underexplored. This paper introduces a simulation framework utilizing the game Balderdash to…

多智能体系统 · 计算机科学 2024-11-18 Parsa Hejabi , Elnaz Rahmati , Alireza S. Ziabari , Preni Golazizian , Jesse Thomason , Morteza Dehghani

Large Language Model (LLM) agents are increasingly used in many applications, raising concerns about their safety. While previous work has shown that LLMs can deceive in controlled tasks, less is known about their ability to deceive using…

人工智能 · 计算机科学 2026-01-21 Christopher Kao , Vanshika Vats , James Davis

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…

计算与语言 · 计算机科学 2026-04-22 Sander Noels , Alexander Rogiers , Maarten Buyl , Tijl De Bie

Quantifying the deceptive potential of Large Language Models (LLMs) is critical for AI safety, yet difficult to achieve in uncontrolled environments. This work investigates the reasoning, persuasion, and deceptive capabilities of LLMs…

计算与语言 · 计算机科学 2026-05-25 Niklas Bauer

We explore how large language models (LLMs) can be influenced by prompting them to alter their initial decisions and align them with established ethical frameworks. Our study is based on two experiments designed to assess the susceptibility…

计算与语言 · 计算机科学 2024-11-19 Allison Huang , Yulu Niki Pi , Carlos Mougan

The development and popularization of large language models (LLMs) have raised concerns that they will be used to create tailor-made, convincing arguments to push false or misleading narratives online. Early work has found that language…

计算机与社会 · 计算机科学 2025-05-21 Francesco Salvi , Manoel Horta Ribeiro , Riccardo Gallotti , Robert West

While artificial intelligence (AI) technology is becoming increasingly popular, its underlying mechanisms tend to remain opaque to most people. To address this gap, the field of AI literacy aims to develop various resources to teach people…

计算机与社会 · 计算机科学 2026-03-31 Allison Chen , Isabella Pu

By formally defining the training processes of large language models (LLMs), which usually encompasses pre-training, supervised fine-tuning, and reinforcement learning with human feedback, within a single and unified machine learning…

计算与语言 · 计算机科学 2024-02-14 Yang Liu , Peng Sun , Hang Li
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