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

arXiv: https://arxiv.org/abs/2407.02351 · HTML: https://mlsanity.com/paper/2407.02351/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- Builds on 80 of its 139 references that are in the map.
- First posted on arXiv 2 Jul 2024.

## Builds on (most cited first, 30 of 80)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://mlsanity.com/paper/2005.11401/) (2020)
- [ReAct: Synergizing Reasoning and Acting in Language Models](https://mlsanity.com/paper/2210.03629/) (2022)
- [mT5: A massively multilingual pre-trained text-to-text transformer](https://mlsanity.com/paper/2010.11934/) (2020)
- [MS MARCO: A Human Generated MAchine Reading COmprehension Dataset](https://mlsanity.com/paper/1611.09268/) (2016)
- [FEVER: a large-scale dataset for Fact Extraction and VERification](https://mlsanity.com/paper/1803.05355/) (2018)
- [Atlas: Few-shot Learning with Retrieval Augmented Language Models](https://mlsanity.com/paper/2208.03299/) (2022)
- [A Multitask, Multilingual, Multimodal Evaluation of ChatGPT on Reasoning, Hallucination, and Interactivity](https://mlsanity.com/paper/2302.04023/) (2023)
- [An Empirical Study of Catastrophic Forgetting in Large Language Models During Continual Fine-tuning](https://mlsanity.com/paper/2308.08747/) (2023)
- [KILT: a Benchmark for Knowledge Intensive Language Tasks](https://mlsanity.com/paper/2009.02252/) (2020)
- [Fact or Fiction: Verifying Scientific Claims](https://mlsanity.com/paper/2004.14974/) (2020)
- [TabFact: A Large-scale Dataset for Table-based Fact Verification](https://mlsanity.com/paper/1909.02164/) (2019)
- [A Survey on Automated Fact-Checking](https://mlsanity.com/paper/2108.11896/) (2021)
- ["Liar, Liar Pants on Fire": A New Benchmark Dataset for Fake News Detection](https://mlsanity.com/paper/1705.00648/) (2017)
- [FakeNewsNet: A Data Repository with News Content, Social Context and Spatialtemporal Information for Studying Fake News on Social Media](https://mlsanity.com/paper/1809.01286/) (2018)
- [Get Your Vitamin C! Robust Fact Verification with Contrastive Evidence](https://mlsanity.com/paper/2103.08541/) (2021)
- [Explainable Automated Fact-Checking for Public Health Claims](https://mlsanity.com/paper/2010.09926/) (2020)
- [HoVer: A Dataset for Many-Hop Fact Extraction And Claim Verification](https://mlsanity.com/paper/2011.03088/) (2020)
- [CLIMATE-FEVER: A Dataset for Verification of Real-World Climate Claims](https://mlsanity.com/paper/2012.00614/) (2020)
- [Can LLM-Generated Misinformation Be Detected?](https://mlsanity.com/paper/2309.13788/) (2023)
- [Fact-Checking Complex Claims with Program-Guided Reasoning](https://mlsanity.com/paper/2305.12744/) (2023)
- [Automated Fact-Checking for Assisting Human Fact-Checkers](https://mlsanity.com/paper/2103.07769/) (2021)
- [AVeriTeC: A Dataset for Real-world Claim Verification with Evidence from the Web](https://mlsanity.com/paper/2305.13117/) (2023)
- [Towards Debiasing Fact Verification Models](https://mlsanity.com/paper/1908.05267/) (2019)
- [WiCE: Real-World Entailment for Claims in Wikipedia](https://mlsanity.com/paper/2303.01432/) (2023)
- [COVID-Fact: Fact Extraction and Verification of Real-World Claims on COVID-19 Pandemic](https://mlsanity.com/paper/2106.03794/) (2021)
- [Towards LLM-based Fact Verification on News Claims with a Hierarchical Step-by-Step Prompting Method](https://mlsanity.com/paper/2310.00305/) (2023)
- [Automated Fact Checking: Task formulations, methods and future directions](https://mlsanity.com/paper/1806.07687/) (2018)
- [X-FACT: A New Benchmark Dataset for Multilingual Fact Checking](https://mlsanity.com/paper/2106.09248/) (2021)
- [Factcheck-Bench: Fine-Grained Evaluation Benchmark for Automatic Fact-checkers](https://mlsanity.com/paper/2311.09000/) (2023)

## Built on by (most cited first, 19 of 19)

- [DEFAME: Dynamic Evidence-based FAct-checking with Multimodal Experts](https://mlsanity.com/paper/2412.10510/) (2024)
- [Hallucination to Truth: A Review of Fact-Checking and Factuality Evaluation in Large Language Models](https://mlsanity.com/paper/2508.03860/) (2025)
- [RFEval: Benchmarking Reasoning Faithfulness under Counterfactual Reasoning Intervention in Large Reasoning Models](https://mlsanity.com/paper/2602.17053/) (2026)
- [Large Language Models for Multilingual Previously Fact-Checked Claim Detection](https://mlsanity.com/paper/2503.02737/) (2025)
- [Assessing the Potential of Generative Agents in Crowdsourced Fact-Checking](https://mlsanity.com/paper/2504.19940/) (2025)
- [Fact-checking with Generative AI: A Systematic Cross-Topic Examination of LLMs Capacity to Detect Veracity of Political Information](https://mlsanity.com/paper/2503.08404/) (2025)
- [TSVer: A Benchmark for Fact Verification Against Time-Series Evidence](https://mlsanity.com/paper/2511.01101/) (2025)
- [A Generative-AI-Driven Claim Retrieval System Capable of Detecting and Retrieving Claims from Social Media Platforms in Multiple Languages](https://mlsanity.com/paper/2504.20668/) (2025)
- [Entailed Opinion Matters: Improving the Fact-Checking Performance of Language Models by Relying on their Entailment Ability](https://mlsanity.com/paper/2505.15050/) (2025)
- [Multilingual vs Crosslingual Retrieval of Fact-Checked Claims: A Tale of Two Approaches](https://mlsanity.com/paper/2505.22118/) (2025)
- [CANDY: Benchmarking LLMs' Limitations and Assistive Potential in Chinese Misinformation Fact-Checking](https://mlsanity.com/paper/2509.03957/) (2025)
- [Mitigating hallucinations in healthcare LLMs with granular fact-checking and domain-specific adaptation](https://mlsanity.com/paper/2512.16189/) (2025)
- [Multilingual Fact-Checking at Scale: Fine-Tuned Compact Models vs LLMs](https://mlsanity.com/paper/2606.08605/) (2026)
- [Leveraging Large Language Models for Information Verification -- an Engineering Approach](https://mlsanity.com/paper/2506.18274/) (2025)
- [Automatic Fact-checking in English and Telugu](https://mlsanity.com/paper/2509.26415/) (2025)
- [SourceMinds at CheckThat! 2026: NLI-Grounded Citation Auditing in a Multi-Agent Pipeline for Full Fact-Checking Article Generation](https://mlsanity.com/paper/2607.24802/) (2026)
- [Effects of Transcript Compression on LLM-based Medical Misinformation Detection in Japanese YouTube Videos](https://mlsanity.com/paper/2609.30882/) (2026)
- [OpenFC: Learning Verification Policies towards Open-Search Fact Checking](https://mlsanity.com/paper/2609.33579/) (2026)
- [VEX-Bench: Benchmarking Verification Complexity of LLM-Generated Misinformation](https://mlsanity.com/paper/2609.35028/) (2026)

## Abstract (arXiv)

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.