Claim verification can be a challenging task. In this paper, we present a method to enhance the robustness and reasoning capabilities of automated claim verification through the extraction of short facts from evidence. Our novel approach, FactDetect, leverages Large Language Models (LLMs) to generate concise factual statements from evidence and label these facts based on their semantic relevance to the claim and evidence. The generated facts are then combined with the claim and evidence. To train a lightweight supervised model, we incorporate a fact-detection task into the claim verification process as a multitasking approach to improve both performance and explainability. We also show that augmenting FactDetect in the claim verification prompt enhances performance in zero-shot claim verification using LLMs. Our method demonstrates competitive results in the supervised claim verification model by 15% on the F1 score when evaluated for challenging scientific claim verification datasets. We also demonstrate that FactDetect can be augmented with claim and evidence for zero-shot prompting (AugFactDetect) in LLMs for verdict prediction. We show that AugFactDetect outperforms the baseline with statistical significance on three challenging scientific claim verification datasets with an average of 17.3% performance gain compared to the best performing baselines.
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）
To mitigate the impact of the scarcity of labelled data on fact-checking systems, we focus on few-shot claim verification. Despite recent work on few-shot classification by proposing advanced language models, there is a dearth of research in data annotation prioritisation that improves the selection of the few shots to be labelled for optimal model performance. We propose Active PETs, a novel weighted approach that utilises an ensemble of Pattern Exploiting Training (PET) models based on various language models, to actively select unlabelled data as candidates for annotation. Using Active PETs…
University of Houston（休斯顿大学）；Everest Cyber Security and Analytics, Inc.（珠穆朗玛网络安全与分析公司）
This article describes research on claim verification carried out using a multiple GAN-based model. The proposed model consists of three pairs of generators and discriminators. The generator and discriminator pairs are responsible for generating synthetic data for supported and refuted claims and claim labels. A theoretical discussion about the proposed model is provided to validate the equilibrium state of the model. The proposed model is applied to the FEVER dataset, and a pre-trained language model is used for the input text data. The synthetically generated data helps to gain information w…
Informatics Institute（信息学研究所）；University of Amsterdam（阿姆斯特丹大学）
Motivated by the promising performance of pre-trained language models, we investigate BERT in an evidence retrieval and claim verification pipeline for the FEVER fact extraction and verification challenge. To this end, we propose to use two BERT models, one for retrieving potential evidence sentences supporting or rejecting claims, and another for verifying claims based on the predicted evidence sets. To train the BERT retrieval system, we use pointwise and pairwise loss functions, and examine the effect of hard negative mining. A second BERT model is trained to classify the samples as support…
Veri-R1: Toward Precise and Faithful Claim Verification via Online Reinforcement Learning
University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；Fudan University（复旦大学）；Tsinghua University（清华大学）；Hong Kong University of Science and Technology（香港科学与技术大学）
CRAVE: A Conflicting Reasoning Approach for Explainable Claim Verification Using LLMs
School of Computer Science, Shanghai Jiao Tong University, Shanghai, China（上海交通大学计算机科学学院）；Shanghai KEY Laboratory of Integrated Administration Technologies for Information Security, Shanghai, China（上海信息安全集成管理技术重点实验室）；School of Politics, National Defense University, Shanghai, China（国防大学政治学院）
Fact in Fragments: Deconstructing Complex Claims via LLM-based Atomic Fact Extraction and Verification
Key Laboratory of Trustworthy Distributed Computing and Service (MoE)（可信分布式计算与服务重点实验室）；Beijing Academy of Artificial Intelligence（北京人工智能研究院）；Jinan University（暨南大学）；Department of Gastric Surgery, Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital(Fujian Branch of Fudan University Shanghai Cancer Center)（福建医科大学胃外科临床肿瘤学院、福建肿瘤医院（复旦大学上海肿瘤中心福建分部））
Debating Truth: Debate-driven Claim Verification with Multiple Large Language Model Agents
Department of Interactive Media, Hong Kong Baptist University（香港浸会大学互动媒体系）；School of Computing and Data Science, The University of Hong Kong（香港大学计算与数据科学学院）；College of Computer Science and Artificial Intelligence, Fudan University（复旦大学计算机科学与人工智能学院）；Beijing Normal-Hong Kong Baptist University（北京师范大学-香港浸会大学联合国际学院）；Shenzhen Institute of Advanced Technology（深圳先进技术研究院）
Assessing the Reasoning Capabilities of LLMs in the context of Evidence-based Claim Verification
University of Warwick（沃里克大学）；Queen Mary University of London（伦敦大学女王学院）；University of Bologna（博洛尼亚大学）；King’s College London（伦敦大学国王学院）；The Alan Turing Institute（艾伦·图灵研究所）
How Robust Is Multimodal Claim Verification to LLM Rewriting?
NII LLMC, Japan（国立情报学研究所 LLMC，日本）；National Institute of Informatics, Japan（国立情报学研究所，日本）；University of Göttingen, Germany（哥廷根大学，德国）；University of Bologna, Italy（博洛尼亚大学，意大利）；The University of Tokyo, Japan（东京大学，日本）；Inria, LS2N, Nantes Université, France（法国国家信息与自动化研究所，LS2N，南特大学，法国）
FactReview: Evidence-Grounded Peer Review with Execution-Based Claim Verification
Rensselaer Polytechnic Institute（罗切斯特理工学院）；Sun Yat-sen University（中山大学）；Southeast University（东南大学）；The Hong Kong University of Science and Technology（香港科技大学）
CHECKWHY: Causal Fact Verification via Argument Structure
Shandong Computer Science Center, Qilu University of Technology (Shandong Academy of Sciences)（齐鲁工业大学（山东省科学院）山东省计算机科学中心）；School of Computer Science and Engineering, Southeast University（东南大学计算机科学与工程学院）
EAVer: Long-Form Factuality Verification as an End-to-End Agentic Policy
University of Illinois Chicago（伊利诺伊大学芝加哥分校）；Taobao & Tmall Group（淘宝天猫集团）；Shandong University（山东大学）；Peking University（北京大学）；The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））
