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Large language models (LLMs) can reproduce a wide variety of rhetorical styles and generate text that expresses a broad spectrum of sentiments. This capacity, now available at low cost, makes them powerful tools for manipulation and…

社会与信息网络 · 计算机科学 2024-05-08 Yaqub Chaudhary , Jonnie Penn

Large language models (LLMs) show increasingly advanced emergent capabilities and are being incorporated across various societal domains. Understanding their behavior and reasoning abilities therefore holds significant importance. We argue…

Decision-making is a fundamental capability in everyday life. Large Language Models (LLMs) provide multifaceted support in enhancing human decision-making processes. However, understanding the influencing factors of LLM-assisted…

人工智能 · 计算机科学 2024-02-28 Eva Eigner , Thorsten Händler

Human decision-making belongs to the foundation of our society and civilization, but we are on the verge of a future where much of it will be delegated to artificial intelligence. The arrival of Large Language Models (LLMs) has transformed…

人工智能 · 计算机科学 2025-06-23 Hao Li , Gengrui Zhang , Petter Holme , Shuyue Hu , Zhen Wang

Large language models (LLMs) have the potential to aid and improve human decision-making in classification tasks, not only by providing fairly accurate predictions, but also in their ability to generate cogent narrative explanations of…

人机交互 · 计算机科学 2026-05-25 Laura R. Marusich , Mary Grace Kozuch Dhooghe , Jonathan Z. Bakdash , Murat Kantarcioglu

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

When we read, we make predictions about upcoming words; these predictions influence our reading behavior. The success of large language models (LLMs), which, like humans, make predictions about upcoming words, has motivated their use as…

计算与语言 · 计算机科学 2026-05-27 Byung-Doh Oh , Tal Linzen

When Artificial Intelligence (AI) is used to replace consumers (e.g., synthetic data), it is often assumed that AI emulates established consumers, and more generally human behaviors. Ten experiments with Large Language Models (LLMs)…

Large Language Models (LLMs) are transforming human decision-making by acting as cognitive collaborators. Yet, this promise comes with a paradox: while LLMs can improve accuracy, they may also erode independent reasoning, promote…

密码学与安全 · 计算机科学 2025-09-09 Irdin Pekaric , Philipp Zech , Tom Mattson

The emergence of Large Language Models (LLMs), has opened exciting possibilities for constructing computational simulations designed to replicate human behavior accurately. Current research suggests that LLM-based agents become increasingly…

计算与语言 · 计算机科学 2024-12-18 Amir Taubenfeld , Yaniv Dover , Roi Reichart , Ariel Goldstein

As large language models (LLMs) achieve advanced persuasive capabilities, concerns about their potential risks have grown. The EU AI Act prohibits AI systems that use manipulative or deceptive techniques to undermine informed…

计算机与社会 · 计算机科学 2025-05-20 Haein Kong

Are large language models (LLMs) biased in favor of communications produced by LLMs, leading to possible antihuman discrimination? Using a classical experimental design inspired by employment discrimination studies, we tested widely used…

计算与语言 · 计算机科学 2025-08-12 Walter Laurito , Benjamin Davis , Peli Grietzer , Tomáš Gavenčiak , Ada Böhm , Jan Kulveit

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

Human communication is motivated: people speak, write, and create content with a particular communicative intent in mind. As a result, information that large language models (LLMs) and AI agents process is inherently framed by humans'…

计算与语言 · 计算机科学 2026-02-03 Addison J. Wu , Ryan Liu , Kerem Oktar , Theodore R. Sumers , Thomas L. Griffiths

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

In order for AI systems to communicate effectively with people, they must understand how we make decisions. However, people's decisions are not always rational, so the implicit internal models of human decision-making in Large Language…

计算与语言 · 计算机科学 2025-03-11 Ryan Liu , Jiayi Geng , Joshua C. Peterson , Ilia Sucholutsky , Thomas L. Griffiths

We explore the potential of Large Language Models (LLMs) to replicate human behavior in economic market experiments. Compared to previous studies, we focus on dynamic feedback between LLM agents: the decisions of each LLM impact the market…

综合经济学 · 经济学 2025-05-13 R. Maria del Rio-Chanona , Marco Pangallo , Cars Hommes

Large Language Models (LLMs) are already as persuasive as humans. However, we know very little about how they do it. This paper investigates the persuasion strategies of LLMs, comparing them with human-generated arguments. Using a dataset…

计算与语言 · 计算机科学 2024-04-23 Carlos Carrasco-Farre

Artificial intelligence (AI) tools such as large language models (LLMs) are already altering student learning. Unlike previous technologies, LLMs can independently solve problems regardless of student understanding, yet are not always…

理论经济学 · 经济学 2025-09-04 Eric Gao

Do generative AI models, particularly large language models (LLMs), exhibit systematic behavioral biases in economic and financial decisions? If so, how can these biases be mitigated? Drawing on the cognitive psychology and experimental…

综合经济学 · 经济学 2026-02-11 Pietro Bini , Lin William Cong , Xing Huang , Lawrence J. Jin
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