Results 271 to 280 of about 4,568,849 (333)

The CLEF-2025 CheckThat! Lab: Subjectivity, Fact-Checking, Claim Normalization, and Retrieval

European Conference on Information Retrieval
The CheckThat! lab aims to advance the development of innovative technologies designed to identify and counteract online disinformation and manipulation efforts across various languages and platforms.
Firoj Alam, J. Struß, Tanmoy Chakraborty, Stefan Dietze, Salim Hafid, Katerina Korre, Arianna Muti, Preslav Nakov, Federico Ruggeri, Sebastian Schellhammer, Vinay Setty, Megha Sundriyal, Konstantin Todorov, V. Venktesh   +13 more
semanticscholar   +1 more source

FACT-AUDIT: An Adaptive Multi-Agent Framework for Dynamic Fact-Checking Evaluation of Large Language Models

Annual Meeting of the Association for Computational Linguistics
Large Language Models (LLMs) have significantly advanced the fact-checking studies. However, existing automated fact-checking evaluation methods rely on static datasets and classification metrics, which fail to automatically evaluate the justification ...
Hongzhan Lin, Yang Deng, Yuxuan Gu, Wenxuan Zhang, Jing Ma, See-Kiong Ng, Tat-Seng Chua   +6 more
semanticscholar   +1 more source

A Hybrid Framework Integrating LLM and ANFIS for Explainable Fact-Checking

IEEE transactions on fuzzy systems
The widespread utilization of social media for information consumption has significantly exacerbated the problem of information disorder. Recognizing the difficulty people face in discerning the truth, automated assistance is urgently needed.
M. Bangerter, G. Fenza, D. Furno, Mariacristina Gallo, V. Loia, Claudio Stanzione, Ilsun You   +6 more
semanticscholar   +1 more source

TrumorGPT: Graph-Based Retrieval-Augmented Large Language Model for Fact-Checking

IEEE Transactions on Artificial Intelligence
In the age of social media, the rapid spread of misinformation and rumors has led to the emergence of infodemics, where false information poses a significant threat to society.
Ching Nam Hang, Pei-Duo Yu, C. Tan
semanticscholar   +1 more source

Hallucination to truth: a review of fact-checking and factuality evaluation in large language models

Artificial Intelligence Review
Large language models (LLMs) are trained on vast and diverse internet corpora that often include inaccurate or misleading content. Consequently, LLMs can generate misinformation, making robust fact-checking essential.
Subhey Sadi Rahman, Md. Adnanul Islam, Md. Mahbub Alam, Musarrat Zeba, Md. Abdur Rahman, Sadia Sultana Chowa, Mohaimenul Azam Khan Raiaan, Sami Azam   +7 more
semanticscholar   +1 more source

Fact-Checking the Output of Large Language Models via Token-Level Uncertainty Quantification

Annual Meeting of the Association for Computational Linguistics
Large language models (LLMs) are notorious for hallucinating, i.e., producing erroneous claims in their output. Such hallucinations can be dangerous, as occasional factual inaccuracies in the generated text might be obscured by the rest of the output ...
Ekaterina Fadeeva, Aleksandr Rubashevskii, Artem Shelmanov, Sergey Petrakov, Haonan Li, Hamdy Mubarak, Evgenii Tsymbalov, Gleb Kuzmin, Alexander Panchenko, Timothy Baldwin, Preslav Nakov, Maxim Panov   +11 more
semanticscholar   +1 more source

Assessing the Potential of Generative Agents in Crowdsourced Fact-Checking

Online Soc. Networks Media
The growing spread of online misinformation has created an urgent need for scalable, reliable fact-checking solutions. Crowdsourced fact-checking - where non-experts evaluate claim veracity - offers a cost-effective alternative to expert verification ...
Luigia Costabile, Gian Marco Orlando, Valerio La Gatta, Vincenzo Moscato   +3 more
semanticscholar   +1 more source

BiDeV: Bilateral Defusing Verification for Complex Claim Fact-Checking

AAAI Conference on Artificial Intelligence
Complex claim fact-checking performs a crucial role in disinformation detection. However, existing fact-checking methods struggle with claim vagueness, specifically in effectively handling latent information and complex relations within claims. Moreover,
Yuxuan Liu, Hongda Sun, Wenya Guo, Xinyan Xiao, Cunli Mao, Zhengtao Yu, Rui Yan   +6 more
semanticscholar   +1 more source

FIRE: Fact-checking with Iterative Retrieval and Verification

North American Chapter of the Association for Computational Linguistics
Fact-checking long-form text is challenging, and it is therefore common practice to break it down into multiple atomic claims. The typical approach to fact-checking these atomic claims involves retrieving a fixed number of pieces of evidence, followed by
Zhuohan Xie, Rui Xing, Yuxia Wang, Jiahui Geng, Hasan Iqbal, Dhruv Sahnan, Iryna Gurevych, Preslav Nakov   +7 more
semanticscholar   +1 more source

DEFAME: Dynamic Evidence-based FAct-checking with Multimodal Experts

International Conference on Machine Learning
The proliferation of disinformation demands reliable and scalable fact-checking solutions. We present Dynamic Evidence-based FAct-checking with Multimodal Experts (DEFAME), a modular, zero-shot MLLM pipeline for open-domain, text-image claim verification.
Tobias Braun, Mark Rothermel, Marcus Rohrbach, A. Rohrbach   +3 more
semanticscholar   +1 more source