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相关论文: Understanding Inequality of LLM Fact-Checking over…

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Automated fact-checking, using machine learning to verify claims, has grown vital as misinformation spreads beyond human fact-checking capacity. Large Language Models (LLMs) like GPT-4 are increasingly trusted to write academic papers,…

计算与语言 · 计算机科学 2024-02-08 Dorian Quelle , Alexandre Bovet

The increasing prevalence of online misinformation has heightened the demand for automated fact-checking solutions. Large Language Models (LLMs) have emerged as potential tools for assisting in this task, but their effectiveness remains…

计算机与社会 · 计算机科学 2025-03-10 Nicolo' Fontana , Francesco Corso , Enrico Zuccolotto , Francesco Pierri

The purpose of this study is to assess how large language models (LLMs) can be used for fact-checking and contribute to the broader debate on the use of automated means for veracity identification. To achieve this purpose, we use AI…

Large language models (LLMs) have raised hopes for automated end-to-end fact-checking, but prior studies report mixed results. As mainstream chatbots increasingly ship with reasoning capabilities and web search tools -- and millions of…

计算与语言 · 计算机科学 2025-11-25 Matthew R. DeVerna , Kai-Cheng Yang , Harry Yaojun Yan , Filippo Menczer

The dissemination of false information on online platforms presents a serious societal challenge. While manual fact-checking remains crucial, Large Language Models (LLMs) offer promising opportunities to support fact-checkers with their…

计算与语言 · 计算机科学 2024-10-31 Ivan Vykopal , Matúš Pikuliak , Simon Ostermann , Marián Šimko

The proliferation of misinformation necessitates scalable, automated fact-checking solutions. Yet, current benchmarks often overlook multilingual and topical diversity. This paper introduces a novel, dynamically extensible data set that…

计算机与社会 · 计算机科学 2025-10-22 Lorraine Saju , Arnim Bleier , Jana Lasser , Claudia Wagner

In our era of widespread false information, human fact-checkers often face the challenge of duplicating efforts when verifying claims that may have already been addressed in other countries or languages. As false information transcends…

计算与语言 · 计算机科学 2025-09-25 Ivan Vykopal , Matúš Pikuliak , Simon Ostermann , Tatiana Anikina , Michal Gregor , Marián Šimko

Our society is facing rampant misinformation harming public health and trust. To address the societal challenge, we introduce FACT-GPT, a system leveraging Large Language Models (LLMs) to automate the claim matching stage of fact-checking.…

计算与语言 · 计算机科学 2024-02-09 Eun Cheol Choi , Emilio Ferrara

The increasing reliance on AI-driven solutions, particularly Large Language Models (LLMs) like the GPT series, for information retrieval highlights the critical need for their factuality and fairness, especially amidst the rampant spread of…

计算与语言 · 计算机科学 2024-02-01 Shujaat Mirza , Bruno Coelho , Yuyuan Cui , Christina Pöpper , Damon McCoy

The rise of misinformation underscores the need for scalable and reliable fact-checking solutions. Large language models (LLMs) hold promise in automating fact verification, yet their effectiveness across global contexts remains uncertain.…

Large Language Models (LLMs) are increasingly used for accessing information on the web. Their truthfulness and factuality are thus of great interest. To help users make the right decisions about the information they get, LLMs should not…

计算与语言 · 计算机科学 2024-04-03 Chenglei Si , Navita Goyal , Sherry Tongshuang Wu , Chen Zhao , Shi Feng , Hal Daumé , Jordan Boyd-Graber

Fact-checking is necessary to address the increasing volume of misinformation. Traditional fact-checking relies on manual analysis to verify claims, but it is slow and resource-intensive. This study establishes baseline comparisons for…

计算与语言 · 计算机科学 2025-02-14 Premtim Sahitaj , Iffat Maab , Junichi Yamagishi , Jawan Kolanowski , Sebastian Möller , Vera Schmitt

In today's digital era, the rapid spread of misinformation poses threats to public well-being and societal trust. As online misinformation proliferates, manual verification by fact checkers becomes increasingly challenging. We introduce…

计算与语言 · 计算机科学 2023-10-16 Eun Cheol Choi , Emilio Ferrara

In this paper, we evaluate the ability of Large Language Models (LLMs) to assess the veracity of claims in ''news reports'' generated by themselves or other LLMs. Our goal is to determine whether LLMs can effectively fact-check their own…

计算与语言 · 计算机科学 2025-03-25 Jiayi Yao , Haibo Sun , Nianwen Xue

Large language models (LLMs) have shown remarkable capabilities in various natural language processing tasks, yet they often struggle with maintaining factual accuracy, particularly in knowledge-intensive domains like healthcare. This study…

计算与语言 · 计算机科学 2024-11-01 Hieu Tran , Junda Wang , Yujan Ting , Weijing Huang , Terrence Chen

Knowledge Graphs (KGs) store structured factual knowledge by linking entities through relationships, crucial for many applications. These applications depend on the KG's factual accuracy, so verifying facts is essential, yet challenging.…

数据库 · 计算机科学 2026-02-12 Farzad Shami , Stefano Marchesin , Gianmaria Silvello

Fact-checking is an essential task in NLP that is commonly utilized for validating the factual accuracy of claims. Prior work has mainly focused on fine-tuning pre-trained languages models on specific datasets, which can be computationally…

计算与语言 · 计算机科学 2024-04-02 Miaoran Li , Baolin Peng , Michel Galley , Jianfeng Gao , Zhu Zhang

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

Multimodal large language models (MLLMs) carry the potential to support humans in processing vast amounts of information. While MLLMs are already being used as a fact-checking tool, their abilities and limitations in this regard are…

计算与语言 · 计算机科学 2024-04-29 Jiahui Geng , Yova Kementchedjhieva , Preslav Nakov , Iryna Gurevych

Large language models (LLMs) often generate content that contains factual errors when responding to fact-seeking prompts on open-ended topics. To benchmark a model's long-form factuality in open domains, we first use GPT-4 to generate…

计算与语言 · 计算机科学 2024-11-08 Jerry Wei , Chengrun Yang , Xinying Song , Yifeng Lu , Nathan Hu , Jie Huang , Dustin Tran , Daiyi Peng , Ruibo Liu , Da Huang , Cosmo Du , Quoc V. Le
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