Evidence plays a crucial role in automated fact-checking. When verifying real-world claims, existing fact-checking systems either assume the evidence sentences are given or use the search snippets returned by the search engine. Such methods ignore the challenges of collecting evidence and may not provide sufficient information to verify real-world claims. Aiming at building a better fact-checking system, we propose to incorporate full text from source documents as evidence and introduce two enriched datasets. The first one is a multilingual dataset, while the second one is monolingual (English). We further develop a latent variable model to jointly extract evidence sentences from documents and perform claim verification. Experiments indicate that including source documents can provide sufficient contextual clues even when gold evidence sentences are not annotated. The proposed system is able to achieve significant improvements upon best-reported models under different settings.
Carnegie Mellon University（卡内基梅隆大学）；Salesforce AI Research（Salesforce人工智能研究院）
Fact-checking is an essential tool to mitigate the spread of misinformation and disinformation. We introduce the task of fact-checking in dialogue, which is a relatively unexplored area. We construct DialFact, a testing benchmark dataset of 22,245 annotated conversational claims, paired with pieces of evidence from Wikipedia. There are three sub-tasks in DialFact: 1) Verifiable claim detection task distinguishes whether a response carries verifiable factual information; 2) Evidence retrieval task retrieves the most relevant Wikipedia snippets as evidence; 3) Claim verification task predicts a …
In this paper, we present the Logically submissions to De-Factify 2 challenge (DE-FACTIFY 2023) on the task 1 of Multi-Modal Fact Checking. We describes our submissions to this challenge including explored evidence retrieval and selection techniques, pre-trained cross-modal and unimodal models, and a cross-modal veracity model based on the well established Transformer Encoder (TE) architecture which is heavily relies on the concept of self-attention. Exploratory analysis is also conducted on this Factify 2 data set that uncovers the salient multi-modal patterns and hypothesis motivating the ar…
MIT Computer Science and Artificial Intelligence Laboratory（麻省理工学院计算机科学与人工智能实验室）；Qatar Computing Research Institute, HBKU（哈马德·本·哈利法大学卡塔尔计算研究所）；Amazon（亚马逊）
A reasonable approach for fact checking a claim involves retrieving potentially relevant documents from different sources (e.g., news websites, social media, etc.), determining the stance of each document with respect to the claim, and finally making a prediction about the claim's factuality by aggregating the strength of the stances, while taking the reliability of the source into account. Moreover, a fact checking system should be able to explain its decision by providing relevant extracts (rationales) from the documents. Yet, this setup is not directly supported by existing datasets, which …
Tsinghua University（清华大学）；University of Illinois at Chicago（伊利诺伊大学芝加哥分校）；University of Cambridge（剑桥大学）
The explosion of misinformation spreading in the media ecosystem urges for automated fact-checking. While misinformation spans both geographic and linguistic boundaries, most work in the field has focused on English. Datasets and tools available in other languages, such as Chinese, are limited. In order to bridge this gap, we construct CHEF, the first CHinese Evidence-based Fact-checking dataset of 10K real-world claims. The dataset covers multiple domains, ranging from politics to public health, and provides annotated evidence retrieved from the Internet. Further, we develop established basel…
University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）
Fact-checking has gained increasing attention due to the widespread of falsified information. Most fact-checking approaches focus on claims made in English only due to the data scarcity issue in other languages. The lack of fact-checking datasets in low-resource languages calls for an effective cross-lingual transfer technique for fact-checking. Additionally, trustworthy information in different languages can be complementary and helpful in verifying facts. To this end, we present the first fact-checking framework augmented with cross-lingual retrieval that aggregates evidence retrieved from m…
AfrIFact: Cultural Information Retrieval, Evidence Extraction and Fact Checking for African Languages
Masakhane NLP；Saarland University（萨尔兰大学）；Saarland Informatic Campus（萨尔兰信息学院）；National Institute of Informatics（国家信息研究所）；University of Waterloo（滑铁卢大学）；University College London（伦敦大学学院）；Instituto Politécnico Nacional（国家理工学院）；University of Ilorin（伊洛林大学）；Universidad de Deusto（德尤斯大学）；Bayero University（贝亚罗大学）；University of Lagos（拉各斯大学）；Black Swan（黑天鹅）；Addis Ababa University（亚的斯亚贝巴大学）；Universite Alioune Diop（阿利乌内·迪普大学）；McGill University（麦吉尔大学）
CheckRLM: Effective Knowledge-Thought Coherence Checking in Retrieval-Augmented Reasoning
School of Artificial Intelligence, Beijing Normal University（北京师范大学人工智能学院）；Beijing Key Laboratory of Artificial Intelligence for Education（北京人工智能教育重点实验室）；Institute of Information Engineering, Chinese Academy of Sciences（中国科学院信息工程研究所）；School of Cyber Security, University of Chinese Academy of Sciences（中国科学院大学网络安全学院）；Department of Computer Science and Technology, Institute for AI, Tsinghua University（清华大学人工智能研究院计算机科学与技术系）；Northeastern University（东北大学）
Columbia University（哥伦比亚大学）；Facebook AI（Facebook人工智能研究院）；George Washington University（乔治华盛顿大学）
The increased focus on misinformation has spurred development of data and systems for detecting the veracity of a claim as well as retrieving authoritative evidence. The Fact Extraction and VERification (FEVER) dataset provides such a resource for evaluating end-to-end fact-checking, requiring retrieval of evidence from Wikipedia to validate a veracity prediction. We show that current systems for FEVER are vulnerable to three categories of realistic challenges for fact-checking -- multiple propositions, temporal reasoning, and ambiguity and lexical variation -- and introduce a resource with th…
TrumorGPT: Graph-Based Retrieval-Augmented Large Language Model for Fact-Checking
Yam Pak Charitable Foundation School of Computing and Information Sciences, Saint Francis University, Hong Kong（Yam Pak Charitable Foundation 计算与信息科学学院，圣弗朗西斯大学，香港）；Department of Applied Mathematics, Chung Yuan Christian University（应用数学系， Chung Yuan Christian University）；College of Computing and Data Science, Nanyang Technological University（计算与数据科学学院，南洋理工大学）
The CLEF-2026 CheckThat! Lab: Advancing Multilingual Fact-Checking
University of Applied Sciences Potsdam, Germany（应用科学大学波茨坦，德国）；GESIS - Leibniz Institute for the Social Sciences, Cologne, Germany（GESIS—社会科学院莱比锡研究所，德国）；Stockholm University, Sweden（斯德哥尔摩大学，瑞典）；University of Stavanger, Norway（斯塔万格大学，挪威）；Indian Institute of Technology Delhi, India（印度理工学院德里，印度）；Mohamed bin Zayed University of Artificial Intelligence, Abu Dhabi（穆罕默德·本·扎耶德人工智能大学，阿布扎比）；Delft University of Technology, The Netherlands（代尔夫特理工大学，荷兰）；Independent Researcher（独立研究者）；médialab Sciences Po, Paris, France（Sciences Po 媒介实验室，法国）；University of Montpellier, LIRMM, CNRS, Montpellier, France（蒙彼利埃大学，LIRMM，CNRS，法国）
Recent work on open domain question answering (QA) assumes strong supervision of the supporting evidence and/or assumes a blackbox information retrieval (IR) system to retrieve evidence candidates. We argue that both are suboptimal, since gold evidence is not always available, and QA is fundamentally different from IR. We show for the first time that it is possible to jointly learn the retriever and reader from question-answer string pairs and without any IR system. In this setting, evidence retrieval from all of Wikipedia is treated as a latent variable. Since this is impractical to learn fro…
The CLEF-2025 CheckThat! Lab: Subjectivity, Fact-Checking, Claim Normalization, and Retrieval
Qatar Computing Research Institute（卡塔尔计算研究所）；University of Applied Sciences Potsdam（波茨坦应用科学大学）；University of Bologna（博洛尼亚大学）；Mohamed bin Zayed University of Artificial Intelligence（穆罕默德·本·扎耶德人工智能大学）；Indian Institute of Technology Delhi（印度理工学院德里分校）；GESIS - Leibniz Institute for the Social Sciences（GESIS莱布尼茨社会科学研究所）；Heinrich-Heine-University Düsseldorf（杜塞尔多夫海因里希·海涅大学）；Delft University of Technology（代尔夫特理工大学）；Indraprastha Institute of Information Technology（因德拉普拉斯塔信息技术学院）；University of Montpellier（蒙彼利埃大学）
ClimateCheck 2026: Scientific Fact-Checking and Disinformation Narrative Classification of Climate-related Claims
Deutsches Forschungszentrum für Künstliche Intelligenz GmbH (DFKI)（德国人工智能研究中心）；Technische Universität Berlin（柏林工业大学）；Leuphana Universität Lüneburg（吕讷堡大学）；Humboldt-Universität zu Berlin（柏林洪堡大学）
Towards Comprehensive Stage-wise Benchmarking of Large Language Models in Fact-Checking
Department of Computer Science, Hong Kong Baptist University（香港 Baptist 大学计算机科学系）；Salesforce AI Research（Salesforce AI 研究院）；The Hong Kong University of Science and Technology（香港科学与技术大学）；National University of Singapore（新加坡国立大学）；The Education University of Hong Kong（香港教育大学）
BiDeV: Bilateral Defusing Verification for Complex Claim Fact-Checking
Gaoling School of Artificial Intelligence, Renmin University of China（中国人民大学高瓴人工智能学院）；Nankai University（南开大学）；Baidu Inc.（百度公司）；Kunming University of Science and Technology（昆明理工大学）
Hybrid Fact-Checking that Integrates Knowledge Graphs, Large Language Models, and Search-Based Retrieval Agents Improves Interpretable Claim Verification
Automated fact-checking based on machine learning is a promising approach to identify false information distributed on the web. In order to achieve satisfactory performance, machine learning methods require a large corpus with reliable annotations for the different tasks in the fact-checking process. Having analyzed existing fact-checking corpora, we found that none of them meets these criteria in full. They are either too small in size, do not provide detailed annotations, or are limited to a single domain. Motivated by this gap, we present a new substantially sized mixed-domain corpus with a…
