Automated Fact-Checking

Follow this topic Ask about this topic

Automated fact-checking studies how to identify verifiable claims, find relevant evidence, and judge whether claims are supported, refuted, or unresolved. Systems combine check-worthiness classifiers, web or knowledge-graph retrieval, claim decomposition, and evidence-grounded language models; newer work also handles images, video, speech, and multilingual claims. Benchmarks such as FEVER, SciFact, and AVeriTeC test retrieval and verdict quality, but results often shift across domains, languages, and evidence conditions. A central challenge is ensuring that verdicts truly follow the cited evidence while controlling retrieval errors, uncertainty, cost, and benchmark contamination.

Latest papers

139 papers17 papers in the last four weeks, up 6% on the four weeks before. 0.1% of all new papers.

Mar 17, 2026

Multilingual Reference Need Assessment System for Wikipedia

Wikipedia is a critical source of information for millions of users across the Web. It serves as a key resource for large language models, search engines, question-answering systems, and other Web-based applications. In Wikipedia, content needs to be verifiable, meaning that readers can check that claims are backed by references to reliable sources. This depends on manual verification by editors, an effective but labor-intensive process, especially given the high volume of daily edits. To address this challenge, we introduce a multilingual machine learning system to assist editors in identifying claims requiring citations. Our approach is tested in 10 language editions of Wikipedia, outperforming existing benchmarks for reference need assessment. We not only consider machine learning evaluation metrics but also system requirements, allowing us to explore the trade-offs between model accuracy and computational efficiency under real-world infrastructure constraints. We deploy our system in production and release data and code to support further research.

Aitolkyn Baigutanova, Francisco Navas, Pablo Aragon +5

Wikimedia Foundation San Francisco, USA · Wikimedia Foundation Brooklyn, USA · Wikimedia Foundation Barcelona, Spain +3

Multilingual Text Classification Text Classification Automated Fact-Checking

Mar 15, 2026

MALicious INTent Dataset and Inoculating LLMs for Enhanced Disinformation Detection

The intentional creation and spread of disinformation poses a significant threat to public discourse. However, existing English datasets and research rarely address the intentionality behind the disinformation. This work presents MALINT, the first human-annotated English corpus developed in collaboration with expert fact-checkers to capture disinformation and its malicious intent. We utilize our novel corpus to benchmark 12 language models, including small language models (SLMs) such as BERT and large language models (LLMs) like Llama 3.3, on binary and multilabel intent classification tasks. Moreover, inspired by inoculation theory from psychology and communication studies, we investigate whether incorporating knowledge of malicious intent can improve disinformation detection. To this end, we propose intent-based inoculation, an intent-augmented reasoning for LLMs that integrates intent analysis to mitigate the persuasive impact of disinformation. Analysis on six disinformation datasets, five LLMs, and seven languages shows that intent-augmented reasoning improves zero-shot disinformation detection. To support research in intent-aware disinformation detection, we release the MALINT dataset with annotations from each annotation step.

Arkadiusz Modzelewski, Witold Sosnowski, Eleni Papadopulos +4

University of Padua, Italy · Polish-Japanese Academy of Information Technology, Poland · NASK National Research Institute, Poland +1

Fake News Detection Intent Classification LLM Reasoning Automated Fact-Checking

Mar 6, 2026

Plato's Cave: A Human-Centered Research Verification System

The growing publication rate of research papers has created an urgent need for better ways to fact-check information, assess writing quality, and identify unverifiable claims. We present Plato's Cave as an open-source, human-centered research verification system that (i) creates a directed acyclic graph (DAG) from a document, (ii) leverages web agents to assign credibility scores to nodes and edges from the DAG, and (iii) gives a final score by interpreting and evaluating the paper's argumentative structure. We report the system implementation and results on a collected dataset of 104 research papers.

Matheus Kunzler Maldaner, Raul Valle, Junsung Kim +9

University of Florida · Georgia Institute of Technology

Scientific Claim Verification Automated Fact-Checking Computational Argumentation

Mar 6, 2026

DeepFact: Co-Evolving Benchmarks and Agents for Deep Research Factuality

Search-augmented LLM agents can produce deep research reports (DRRs), but verifying claim-level factuality remains challenging. Existing fact-checkers are primarily designed for general-domain, factoid-style atomic claims, and there is no benchmark to test whether such verifiers transfer to DRRs. Yet building such a benchmark is itself difficult. We first show that static expert-labeled benchmarks are brittle in this setting: in a controlled study with PhD-level specialists, unassisted experts achieve only 60.8% accuracy on a hidden micro-gold set of verifiable claims. We propose Evolving Benchmarking via Audit-then-Score (AtS), where benchmark labels and rationales are explicitly revisable: when a verifier disagrees with the current benchmark, it must submit evidence; an auditor adjudicates the dispute; and accepted revisions update the benchmark before models are scored. Across four AtS rounds, expert micro-gold accuracy rises to 90.9%, indicating experts are substantially more reliable as auditors than as one-shot labelers. We instantiate AtS as DeepFact-Bench, a versioned DRR factuality benchmark with auditable rationales, and DeepFact-Eval, a document-level verification agent (with a grouped lite variant) that outperforms existing verifiers on DeepFact-Bench and transfers well to external factuality datasets.

Yukun Huang, Leonardo F. R. Ribeiro, Momchil Hardalov +3

Duke University · Amazon AGI · Boston University

Automated Fact-Checking AI Agent Benchmarks Factual Consistency Evaluation Benchmark Auditing

Mar 5, 2026

Leveraging LLM Parametric Knowledge for Fact Checking without Retrieval

Trustworthiness is a core research challenge for agentic AI systems built on Large Language Models (LLMs). To enhance trust, natural language claims from diverse sources, including human-written text, web content, and model outputs, are commonly checked for factuality by retrieving external knowledge and using an LLM to verify the faithfulness of claims to the retrieved evidence. As a result, such methods are constrained by retrieval errors and external data availability, while leaving the models intrinsic fact-verification capabilities largely unused. We propose the task of fact-checking without retrieval, focusing on the verification of arbitrary natural language claims, independent of their source. To study this setting, we introduce a comprehensive evaluation framework focused on generalization, testing robustness to (i) long-tail knowledge, (ii) variation in claim sources, (iii) multilinguality, and (iv) long-form generation. Across 9 datasets, 18 methods and 3 models, our experiments indicate that logit-based approaches often underperform compared to those that leverage internal model representations. Building on this finding, we introduce INTRA, a method that exploits interactions between internal representations and achieves state-of-the-art performance with strong generalization. More broadly, our work establishes fact-checking without retrieval as a promising research direction that can complement retrieval-based frameworks, improve scalability, and enable the use of such systems as reward signals during training or as components integrated into the generation process.

Artem Vazhentsev, Maria Marina, Daniil Moskovskiy +8

MBZUAI · S-NLP Group · AXXX +3

Automated Fact-Checking Factual Knowledge in Language Models

Mar 3, 2026

A Browser-based Open Source Assistant for Multimodal Content Verification

Disinformation and false content produced by generative AI pose a significant challenge for journalists and fact-checkers who must rapidly verify digital media information. While there is an abundance of NLP models for detecting credibility signals such as persuasion techniques, subjectivity, or machine-generated text, such methods often remain inaccessible to non-expert users and are not integrated into their daily workflows as a unified framework. This paper demonstrates the VERIFICATION ASSISTANT, a browser-based tool designed to bridge this gap. The VERIFICATION ASSISTANT, a core component of the widely adopted VERIFICATION PLUGIN (140,000+ users), allows users to submit URLs or media files to a unified interface. It automatically extracts content and routes it to a suite of backend NLP classifiers, delivering actionable credibility signals, estimating AI-generated content, and providing other verification guidance in a clear, easy-to-digest format. This paper showcases the tool architecture, its integration of multiple NLP services, and its real-world application to detecting disinformation.

Rosanna Milner, Michael Foster, Twin Karmakharm +5

University of Sheffield · AFP Medialab

Automated Fact-Checking Fake News Detection

Feb 27, 2026

Multi-Sourced, Multi-Agent Evidence Retrieval for Fact-Checking

Misinformation spreading over the Internet poses a significant threat to both societies and individuals, necessitating robust and scalable fact-checking that relies on retrieving accurate and trustworthy evidence. Previous methods rely on semantic and social-contextual patterns learned from training data, which limits their generalization to new data distributions. Recently, Retrieval Augmented Generation (RAG) based methods have been proposed to utilize the reasoning capability of LLMs with retrieved grounding evidence documents. However, these methods largely rely on textual similarity for evidence retrieval and struggle to retrieve evidence that captures multi-hop semantic relations within rich document contents. These limitations lead to overlooking subtle factual correlations between the evidence and the claims to be fact-checked during evidence retrieval, thus causing inaccurate veracity predictions. To address these issues, we propose WKGFC, which exploits authorized open knowledge graph as a core resource of evidence. LLM-enabled retrieval is designed to assess the claims and retrieve the most relevant knowledge subgraphs, forming structured evidence for fact verification. To augment the knowledge graph evidence, we retrieve web contents for completion. The above process is implemented as an automatic Markov Decision Process (MDP): A reasoning LLM agent decides what actions to take according to the current evidence and the claims. To adapt the MDP for fact-checking, we use prompt optimization to fine-tune the agentic LLM.

Shuzhi Gong, Richard O. Sinnott, Jianzhong Qi +3

The University of Melbourne, Melbourne, VIC, Australia · Data61, CSIRO, Sydney, NSW, Australia · MBZUAI, Abu Dhabi, UAE

Automated Fact-Checking Evidence Retrieval Agentic RAG Graph Retrieval-Augmented Generation

Feb 26, 2026

FactGuard: Agentic Video Misinformation Detection via Reinforcement Learning

Multimodal large language models (MLLMs) have substantially advanced video misinformation detection through unified multimodal reasoning, but they often rely on fixed-depth inference and place excessive trust in internally generated assumptions, particularly in scenarios where critical evidence is sparse, fragmented, or requires external verification. To address these limitations, we propose FactGuard, an agentic framework for video misinformation detection that formulates verification as an iterative reasoning process built upon MLLMs. FactGuard explicitly assesses task ambiguity and selectively invokes external tools to acquire critical evidence, enabling progressive refinement of reasoning trajectories. To further strengthen this capability, we introduce a two-stage training strategy that combines domain-specific agentic supervised fine-tuning with decision-aware reinforcement learning to optimize tool usage and calibrate risk-sensitive decision making. Extensive experiments on FakeSV, FakeTT, and FakeVV demonstrate FactGuard's state-of-the-art performance and validate its excellent robustness and generalization capacity.

Zehao Li, Hongwei Yu, Hao Jiang +7

Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China · University of Chinese Academy of Sciences, Beijing, China · University of Science and Technology Beijing, Beijing, China +1

Automated Fact-Checking Tool Use in VLMs VLM Reasoning RL for VLMs

Feb 18, 2026

MultiCW: A Large-Scale Balanced Benchmark Dataset for Training Robust Check-Worthiness Detection Models

Large Language Models (LLMs) are beginning to reshape how media professionals verify information, yet automated support for detecting check-worthy claims a key step in the fact-checking process remains limited. We introduce the Multi-Check-Worthy (MultiCW) dataset, a balanced multilingual benchmark for check-worthy claim detection spanning 16 languages, 7 topical domains, and 2 writing styles. It consists of 123,722 samples, evenly distributed between noisy (informal) and structured (formal) texts, with balanced representation of check-worthy and non-check-worthy classes across all languages. To probe robustness, we also introduce an equally balanced out-of-distribution evaluation set of 27,761 samples in 4 additional languages. To provide baselines, we benchmark 3 common fine-tuned multilingual transformers against a diverse set of 15 commercial and open LLMs under zero-shot settings. Our findings show that fine-tuned models consistently outperform zero-shot LLMs on claim classification and show strong out-of-distribution generalization across languages, domains, and styles. MultiCW provides a rigorous multilingual resource for advancing automated fact-checking and enables systematic comparisons between fine-tuned models and cutting-edge LLMs on the check-worthy claim detection task.

Martin Hyben, Sebastian Kula, Jan Cegin +3

Kempelen Institute of Intelligent Technologies, Bratislava, Slovakia · West Pomeranian University of Technology in Szczecin, Szczecin, Poland

Automated Fact-Checking Language Model Robustness Multilingual Language Model Evaluation

Feb 16, 2026

AIC CTU@AVerImaTeC: dual-retriever RAG for image-text fact checking

In this paper, we present our 3rd place system in the AVerImaTeC shared task, which combines our last year's retrieval-augmented generation (RAG) pipeline with a reverse image search (RIS) module. Despite its simplicity, our system delivers competitive performance with a single multimodal LLM call per fact-check at just $0.013 on average using GPT5.1 via OpenAI Batch API. Our system is also easy to reproduce and tweak, consisting of only three decoupled modules - a textual retrieval module based on similarity search, an image retrieval module based on API-accessed RIS, and a generation module using GPT5.1 - which is why we suggest it as an accesible starting point for further experimentation. We publish its code and prompts, as well as our vector stores and insights into the scheme's running costs and directions for further improvement.

Herbert Ullrich, Jan Drchal

Automated Fact-Checking Retrieval-Augmented Generation

Feb 16, 2026

D-SECURE: Dual-Source Evidence Combination for Unified Reasoning in Misinformation Detection

Multimodal misinformation increasingly mixes realistic im-age edits with fluent but misleading text, producing persuasive posts that are difficult to verify. Existing systems usually rely on a single evidence source. Content-based detectors identify local inconsistencies within an image and its caption but cannot determine global factual truth. Retrieval-based fact-checkers reason over external evidence but treat inputs as coarse claims and often miss subtle visual or textual manipulations. This separation creates failure cases where internally consistent fabrications bypass manipulation detectors and fact-checkers verify claims that contain pixel-level or token-level corruption. We present D-SECURE, a framework that combines internal manipulation detection with external evidence-based reasoning for news-style posts. D-SECURE integrates the HAMMER manipulation detector with the DEFAME retrieval pipeline. DEFAME performs broad verification, and HAMMER analyses residual or uncertain cases that may contain fine-grained edits. Experiments on DGM4 and ClaimReview samples highlight the complementary strengths of both systems and motivate their fusion. We provide a unified, explainable report that incorporates manipulation cues and external evidence.

Samudi Amarasinghe, Gagandeep Singh, Priyanka Singh

School of Information Technology and Electrical Engineering, The University of Queensland, Brisbane, Australia

Fake News Detection Automated Fact-Checking

Feb 9, 2026

GitSearch: Enhancing Community Notes Generation with Gap-Informed Targeted Search

Community Notes enable scalable, community-based fact-checking, but automated generation faces a coverage--quality trade-off. Note-dependent methods (e.g., summarization-based Supernotes) cannot operate without prior crowd input, while web-search LLMs achieve broad coverage but often retrieve evidence misaligned with what raters need. We introduce GitSearch (Gap-Informed Targeted Search), which treats missing, ambiguous, or disputed information as first-class retrieval targets. It infers information gaps from a post and optional incomplete notes, searches specifically to resolve them, and synthesizes an evidence-grounded Community Note. To evaluate this, we introduce PolBench, a new dataset of 78,698 U.S. political tweets with Community Notes. Keeping single-call search inference budget fixed, GitSearch improves human-rated helpfulness by 0.43 over unconditioned web search and wins 59% of pairwise comparisons. It generates notes for 99% of test cases, including the 46% where summarization methods fail. Human evaluators prefer GitSearch to human-written helpful notes in 69% of comparisons, primarily due to better context coverage. These findings show that how search is organized matters more than information access alone.

Sahajpreet Singh, Kokil Jaidka, Min-Yen Kan +1

National University of Singapore

Automated Fact-Checking Evidence Retrieval Text Generation

Jan 7, 2026

RADAR: Retrieval-Augmented Detector with Adversarial Refinement for Adaptive LLM-Generated Fake News Detection

To efficiently combat the spread of LLM-generated misinformation in the news domain, we present RADAR, a Retrieval-Augmented Detector with Adversarial Refinement for adaptive LLM-generated fake news detection. Our approach employs a generator that rewrites real articles with factual perturbations, paired with a lightweight detector that verifies claims using dense passage retrieval. To enable effective co-evolution, we introduce Verbal Adversarial Feedback (VAF). Rather than relying on scalar rewards, VAF issues structured natural-language critiques; these guide the generator toward more sophisticated evasion attempts, compelling the detector to adapt and improve. Experiments on an LLM-generated fake news benchmark show that RADAR outperforms retrieval-augmented trainable baselines and general-purpose LLMs with retrieval. Further analysis shows that retrieval on both the generator and detector sides improves performance, while VAF and few-shot demonstrations offer complementary benefits. RADAR also transfers better to fake news generated by an unseen external attacker, suggesting improved generalization beyond the specific co-evolved generator used during training.

Song-Duo Ma, Yi-Hung Liu, Hsin-Yu Lin +4

National Taiwan University, Taipei, Taiwan

Adversarial Training Fake News Detection Text Classification Automated Fact-Checking Adversarial Text Attacks

Dec 29, 2025

Althea: The Fact-Checking--Metalearning Tradeoff in AI-Assisted Verification

Fact-checking systems must be scalable and epistemically trustworthy. We introduce Althea, a retrieval-augmented system for user-driven claim evaluation that matches standard pipelines on AVeriTeC while improving supported/refuted discrimination. A longitudinal survey experiment (N=961) treats a ten-day follow-up as a fading test: after modeling a verification procedure, we remove the system and ask whether users reproduce it unaided, testing metalearning rather than one-time accuracy. We compare two AI-assisted treatments, Exploratory (guided reasoning) and Summary (synthesized verdicts), against two baselines, unrelated news and Self-search. The treatments yield the strongest immediate accuracy and confidence gains but do not survive the fading test: on unseen claims they perform no better than news, while Self-search, with no procedure to fade, retains a large advantage. This reveals a factchecking-metalearning tradeoff: conditions that most improve immediate accuracy are least likely to produce metalearning, cautioning against treating AI-delivered verdicts as a source of durable literacy gains.

Svetlana Churina, Kokil Jaidka, Anab Maulana Barik +5

Centre for Trusted Internet & Community (CTIC), National University of Singapore (NUS), Singapore · Department of Communications and New Media, Centre for Trusted Internet & Community (CTIC), National University of Singapore (NUS), Singapore · School of Computing, Centre for Trusted Internet & Community (CTIC), National University of Singapore (NUS), Singapore

Human-in-the-Loop Evaluation Automated Fact-Checking

Nov 5, 2025

Hybrid Fact-Checking that Integrates Knowledge Graphs, Large Language Models, and Search-Based Retrieval Agents Improves Interpretable Claim Verification

Large language models (LLMs) excel in generating fluent utterances but can lack reliable grounding in verified information. At the same time, knowledge-graph-based fact-checkers deliver precise and interpretable evidence, yet suffer from limited coverage or latency. By integrating LLMs with knowledge graphs and real-time search agents, we introduce a hybrid fact-checking approach that leverages the individual strengths of each component. Our system comprises three autonomous steps: 1) a Knowledge Graph (KG) Retrieval for rapid one-hop lookups in DBpedia, 2) an LM-based classification guided by a task-specific labeling prompt, producing outputs with internal rule-based logic, and 3) a Web Search Agent invoked only when KG coverage is insufficient. Our pipeline achieves an F1 score of 0.93 on the FEVER benchmark on the Supported/Refuted split without task-specific fine-tuning. To address Not enough information cases, we conduct a targeted reannotation study showing that our approach frequently uncovers valid evidence for claims originally labeled as Not Enough Information (NEI), as confirmed by both expert annotators and LLM reviewers. With this paper, we present a modular, opensource fact-checking pipeline with fallback strategies and generalization across datasets.

Shaghayegh Kolli, Richard Rosenbaum, Timo Cavelius +3

Technical University Munich

LLM Grounding Automated Fact-Checking

Oct 27, 2025

M4FC: a Multimodal, Multilingual, Multicultural, Multitask Real-World Fact-Checking Dataset

Existing real-world datasets for multimodal fact-checking have multiple limitations: they contain few instances, cover only one or two languages, focus on a single task, or rely on external news article sets to source true claims. To address these shortcomings, we introduce M4FC, a new real-world dataset comprising 4,982 images paired with 6,980 claims. The images, verified by professional fact-checkers from 22 organizations, represent a diverse range of cultural and geographic contexts. Each claim is available in one or two out of ten languages. M4FC spans six multimodal fact-checking tasks: visual claim extraction, claimant intent prediction, fake image detection, image contextualization, location verification, and verdict prediction. We provide baseline results for all tasks and analyze how combining intermediate tasks affects verdict prediction performance. We make our dataset and code publicly available.

Jiahui Geng, Jonathan Tonglet, Iryna Gurevych

Mohamed bin Zayed University of Artificial Intelligence (MBZUAI), UAE · Ubiquitous Knowledge Processing Lab (UKP Lab), Department of Computer Science, TU Darmstadt and National Research Center for Applied Cybersecurity ATHENE, Germany · Department of Electrical Engineering, KU Leuven, Belgium +2

Automated Fact-Checking Multimodal Understanding Multimodal Dataset

May 21, 2025

UrduFactCheck: An Agentic Fact-Checking Framework for Urdu with Evidence Boosting and Benchmarking

The rapid adoption of Large Language Models (LLMs) has raised important concerns about the factual reliability of their outputs, particularly in low-resource languages such as Urdu. Existing automated fact-checking systems are predominantly developed for English, leaving a significant gap for the more than 200 million Urdu speakers worldwide. In this work, we present UrduFactBench and UrduFactQA, two novel hand-annotated benchmarks designed to enable fact-checking and factual consistency evaluation in Urdu. While UrduFactBench focuses on claim verification, UrduFactQA targets the factuality of LLMs in question answering. These resources, the first of their kind for Urdu, were developed through a multi-stage annotation process involving native Urdu speakers. To complement these benchmarks, we introduce UrduFactCheck, a modular fact-checking framework that incorporates both monolingual and translation-based evidence retrieval strategies to mitigate the scarcity of high-quality Urdu evidence. Leveraging these resources, we conduct an extensive evaluation of twelve LLMs and demonstrate that translation-augmented pipelines consistently enhance performance compared to monolingual ones. Our findings reveal persistent challenges for open-source LLMs in Urdu and underscore the importance of developing targeted resources. All code and data are publicly available at https://github.com/mbzuai-nlp/UrduFactCheck.

Sarfraz Ahmad, Hasan Iqbal, Momina Ahsan +6

MBZUAI

LLM Evaluation Factual Consistency Evaluation Automated Fact-Checking Low-Resource Language Processing Evidence Retrieval

Oct 19, 2024

TrendFact: A Benchmark Towards Hotspot Perception in Automatic Fact-Checking

With the surge of online misinformation, Large Language Models (LLMs) and Reasoning Large Language Models (RLMs) serving as Automatic Fact-Checking (AFC) systems have emerged as a prominent paradigm for reliable, explainable verification. However, our empirical study reveals that this paradigm faces a critical risk asymmetry challenge when deployed in the real world under resource-constrained environments. While Hotspot Perception Ability (HPA), the capacity to dynamically allocate reasoning resources based on social impact, is essential to mitigate this risk, existing benchmarks lack the social metadata and evaluation framework to meet this urgent evaluation needs, thereby hindering the advancement of these AFC systems. To bridge this gap, we introduce TrendFact, the first benchmark capable of evaluating HPA and three fact-checking tasks. It consists of 7,643 curated samples sourced from trending platforms and professional datasets, with an evidence library containing 366,634 entries. To enable HPA assessment, we propose two novel metrics: the Explanation Consistency Score (ECS) to evaluate the reliability of verification reasoning, and the Hotspot Claim Perception Index (HCPI) to quantify the overall HPA of AFC systems. Extensive experiments demonstrate that existing AFC systems exhibit limited performance on TrendFact. Furthermore, our proposed FactISR framework effectively enhances HPA and computational efficiency for RLMs-served AFC systems.

Xiaocheng Zhang, Xi Wang, Yifei Lu +5

1Harbin Institute of Technology · 2National University of Defense Technology · 3Northeastern University +3

Automated Fact-Checking

Jul 2, 2024

Generative Large Language Models in Automated Fact-Checking: A Survey

The rapid spread of false and misleading information on online platforms poses a growing societal challenge, overwhelming the capacity of manual fact-checking and increasing the demand for scalable, reliable automation. Recent advances in generative large language models (LLMs) have broadened the scope of automated fact-checking beyond accuracy-driven prediction. LLMs are now integral components of fact-checking pipelines, supporting tasks such as generating new data, performing and assisting with fact verification, and shaping how fact-checking systems are evaluated. This survey provides a comprehensive overview of the role of generative LLMs in automated fact-checking, based on a systematic review of 199 research papers. We introduce a unifying taxonomy that captures how generative LLMs are integrated into fact-checking workflows and analyze their use across core fact-checking tasks, dataset construction and augmentation strategies, task formulations, and evaluation practices. Additionally, we investigate the impact of generative LLMs in multilingual and low-resource settings in fact-checking, highlighting trends, limitations, and gaps in current research. By consolidating fragmented research efforts and identifying methodological patterns, limitations, and open challenges, this survey maps the current state of generative LLMs in automated fact-checking. It aims to support researchers in developing more reliable, interpretable, and inclusive fact-checking systems, while outlining promising directions for future research in this rapidly evolving field.

Ivan Vykopal, Matúš Pikuliak, Simon Ostermann +1

Faculty of Information Technology, Brno University of Technology, Brno, Czech Republic · Kempelen Institute of Intelligent Technologies, Bratislava, Slovakia · German Research Center for Artificial Intelligence (DFKI), Saarbrücken, Germany +1

Automated Fact-Checking