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Automated disinformation generation is often listed as an important risk associated with large language models (LLMs). The theoretical ability to flood the information space with disinformation content might have dramatic consequences for…

计算与语言 · 计算机科学 2025-06-13 Ivan Vykopal , Matúš Pikuliak , Ivan Srba , Robert Moro , Dominik Macko , Maria Bielikova

This study investigates the generation of synthetic disinformation by OpenAI's Large Language Models (LLMs) through prompt engineering and explores their responsiveness to emotional prompting. Leveraging various LLM iterations using…

人工智能 · 计算机科学 2024-03-07 Rasita Vinay , Giovanni Spitale , Nikola Biller-Andorno , Federico Germani

Large language models (LLMs) make it possible to generate synthetic behavioural data at scale, offering an ethical and low-cost alternative to human experiments. Whether such data can faithfully capture psychological differences driven by…

计算与语言 · 计算机科学 2025-11-27 Manuel Pratelli , Marinella Petrocchi

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

The capabilities of recent large language models (LLMs) to generate high-quality content indistinguishable by humans from human-written texts raises many concerns regarding their misuse. Previous research has shown that LLMs can be…

计算与语言 · 计算机科学 2025-07-28 Aneta Zugecova , Dominik Macko , Ivan Srba , Robert Moro , Jakub Kopal , Katarina Marcincinova , Matus Mesarcik

In this paper, we explore the feasibility of leveraging large language models (LLMs) to automate or otherwise assist human raters with identifying harmful content including hate speech, harassment, violent extremism, and election…

The emergence of Large Language Models (LLMs) presents a dual challenge in the fight against disinformation. These powerful tools, capable of generating human-like text at scale, can be weaponised to produce sophisticated and persuasive…

Increased sophistication of large language models (LLMs) and the consequent quality of generated multilingual text raises concerns about potential disinformation misuse. While humans struggle to distinguish LLM-generated content from…

In this paper, we comprehensively investigate the potential misuse of modern Large Language Models (LLMs) for generating credible-sounding misinformation and its subsequent impact on information-intensive applications, particularly…

计算与语言 · 计算机科学 2023-10-30 Yikang Pan , Liangming Pan , Wenhu Chen , Preslav Nakov , Min-Yen Kan , William Yang Wang

Large Language Models (LLMs) have shown remarkable capabilities in knowledge-intensive tasks, while they remain vulnerable when encountering misinformation. Existing studies have explored the role of LLMs in combating misinformation, but…

计算与语言 · 计算机科学 2025-05-29 Miao Peng , Nuo Chen , Jianheng Tang , Jia Li

Large language models (LLMs) can generate persuasive narratives at scale, raising concerns about their potential use in disinformation campaigns. Assessing this risk ultimately requires understanding how readers receive such content. In…

人工智能 · 计算机科学 2026-04-09 Zonghuan Xu , Xiang Zheng , Yutao Wu , Xingjun Ma

Large Language Models (LLMs) have garnered significant attention for their powerful ability in natural language understanding and reasoning. In this paper, we present a comprehensive empirical study to explore the performance of LLMs on…

计算与语言 · 计算机科学 2024-12-30 Mengyang Chen , Lingwei Wei , Han Cao , Wei Zhou , Songlin Hu

In the digital age, the prevalence of misleading news headlines poses a significant challenge to information integrity, necessitating robust detection mechanisms. This study explores the efficacy of Large Language Models (LLMs) in…

计算与语言 · 计算机科学 2024-05-07 Md Main Uddin Rony , Md Mahfuzul Haque , Mohammad Ali , Ahmed Shatil Alam , Naeemul Hassan

The recent success in language generation capabilities of large language models (LLMs), such as GPT, Bard, Llama etc., can potentially lead to concerns about their possible misuse in inducing mass agitation and communal hatred via…

计算与语言 · 计算机科学 2024-01-10 Shrey Satapara , Parth Mehta , Debasis Ganguly , Sandip Modha

While Large Language Models (LLMs) can amplify online misinformation, they also show promise in tackling misinformation. In this paper, we empirically study the capabilities of three LLMs -- ChatGPT, Gemini, and Claude -- in countering…

计算与语言 · 计算机科学 2025-09-29 Adiba Mahbub Proma , Neeley Pate , James Druckman , Gourab Ghoshal , Hangfeng He , Ehsan Hoque

Large Language Models (LLMs) can generate human-like disinformation, yet their ability to personalise such content across languages and demographics remains underexplored. This study presents the first large-scale, multilingual analysis of…

Large language models (LLMs) are increasingly used as proxies for human judgment in computational social science, yet their ability to reproduce patterns of susceptibility to misinformation remains unclear. We test whether LLM-simulated…

社会与信息网络 · 计算机科学 2026-04-13 Eun Cheol Choi , Lindsay E. Young , Emilio Ferrara

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

Large Language Models (LLMs) are a transformational technology, fundamentally changing how people obtain information and interact with the world. As people become increasingly reliant on them for an enormous variety of tasks, a body of…

计算机与社会 · 计算机科学 2025-05-08 Nouar Aldahoul , Hazem Ibrahim , Matteo Varvello , Aaron Kaufman , Talal Rahwan , Yasir Zaki

We investigate and observe the behaviour and performance of Large Language Model (LLM)-backed chatbots in addressing misinformed prompts and questions with demographic information within the domains of Climate Change and Mental Health.…

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