# FACT-GPT: Fact-Checking Augmentation via Claim Matching with LLMs

arXiv: https://arxiv.org/abs/2402.05904 · HTML: https://mlsanity.com/paper/2402.05904/

## Key facts

- Cited by 27 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 30 references that are in the map.
- First posted on arXiv 8 Feb 2024.

## Builds on (most cited first, 13 of 13)

- [LoRA: Low-Rank Adaptation of Large Language Models](https://mlsanity.com/paper/2106.09685/) (2021)
- [ChatGPT Outperforms Crowd-Workers for Text-Annotation Tasks](https://mlsanity.com/paper/2303.15056/) (2023)
- [ChatGPT: Jack of all trades, master of none](https://mlsanity.com/paper/2302.10724/) (2023)
- [Automated Fact-Checking for Assisting Human Fact-Checkers](https://mlsanity.com/paper/2103.07769/) (2021)
- [Factuality Challenges in the Era of Large Language Models](https://mlsanity.com/paper/2310.05189/) (2023)
- [That is a Known Lie: Detecting Previously Fact-Checked Claims](https://mlsanity.com/paper/2005.06058/) (2020)
- [Testing the Reliability of ChatGPT for Text Annotation and Classification: A Cautionary Remark](https://mlsanity.com/paper/2304.11085/) (2023)
- [Reinforcement Learning-based Counter-Misinformation Response Generation: A Case Study of COVID-19 Vaccine Misinformation](https://mlsanity.com/paper/2303.06433/) (2023)
- [Towards Fine-Grained Reasoning for Fake News Detection](https://mlsanity.com/paper/2110.15064/) (2021)
- [Assisting the Human Fact-Checkers: Detecting All Previously Fact-Checked Claims in a Document](https://mlsanity.com/paper/2109.07410/) (2021)
- [Retrieving false claims on Twitter during the Russia-Ukraine conflict](https://mlsanity.com/paper/2303.10121/) (2023)
- [Construction of Large-Scale Misinformation Labeled Datasets from Social Media Discourse using Label Refinement](https://mlsanity.com/paper/2202.12413/) (2022)
- [Leveraging Social Interactions to Detect Misinformation on Social Media](https://mlsanity.com/paper/2304.02983/) (2023)

## Built on by (most cited first, 27 of 27)

- [LLM-Rubric: A Multidimensional, Calibrated Approach to Automated Evaluation of Natural Language Texts](https://mlsanity.com/paper/2501.00274/) (2024)
- [Retrieval Augmented Fact Verification by Synthesizing Contrastive Arguments](https://mlsanity.com/paper/2406.09815/) (2024)
- [Detecting AI-Generated Text: Factors Influencing Detectability with Current Methods](https://mlsanity.com/paper/2406.15583/) (2024)
- [LLM Uncertainty Quantification through Directional Entailment Graph and Claim Level Response Augmentation](https://mlsanity.com/paper/2407.00994/) (2024)
- [Generative Large Language Models in Automated Fact-Checking: A Survey](https://mlsanity.com/paper/2407.02351/) (2024)
- [Uncertainty Quantification of Large Language Models through Multi-Dimensional Responses](https://mlsanity.com/paper/2502.16820/) (2025)
- [Hallucination to Truth: A Review of Fact-Checking and Factuality Evaluation in Large Language Models](https://mlsanity.com/paper/2508.03860/) (2025)
- [Zero-shot and Few-shot Learning with Instruction-following LLMs for Claim Matching in Automated Fact-checking](https://mlsanity.com/paper/2501.10860/) (2025)
- [RealFactBench: A Benchmark for Evaluating Large Language Models in Real-World Fact-Checking](https://mlsanity.com/paper/2506.12538/) (2025)
- [Limited Effectiveness of LLM-based Data Augmentation for COVID-19 Misinformation Stance Detection](https://mlsanity.com/paper/2503.02328/) (2025)
- [LlamaLens: Specialized Multilingual LLM for Analyzing News and Social Media Content](https://mlsanity.com/paper/2410.15308/) (2024)
- [Large Language Models for Multilingual Previously Fact-Checked Claim Detection](https://mlsanity.com/paper/2503.02737/) (2025)
- [RAMA: Retrieval-Augmented Multi-Agent Framework for Misinformation Detection in Multimodal Fact-Checking](https://mlsanity.com/paper/2507.09174/) (2025)
- [FactGenius: Combining Zero-Shot Prompting and Fuzzy Relation Mining to Improve Fact Verification with Knowledge Graphs](https://mlsanity.com/paper/2406.01311/) (2024)
- [STRIVE: Structured Reasoning for Self-Improvement in Claim Verification](https://mlsanity.com/paper/2502.11959/) (2025)
- [Assessing the Potential of Generative Agents in Crowdsourced Fact-Checking](https://mlsanity.com/paper/2504.19940/) (2025)
- [VERITAS-NLI : Validation and Extraction of Reliable Information Through Automated Scraping and Natural Language Inference](https://mlsanity.com/paper/2410.09455/) (2024)
- [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)
- [How Human Feedback Shapes AI-generated Community Notes](https://mlsanity.com/paper/2606.30905/) (2026)
- [ViMGuard: A Novel Multi-Modal System for Video Misinformation Guarding](https://mlsanity.com/paper/2410.16592/) (2024)
- [Enhancing Multi-Hop Fact Verification with Structured Knowledge-Augmented Large Language Models](https://mlsanity.com/paper/2503.08495/) (2025)
- [(Fact) Check Your Bias](https://mlsanity.com/paper/2506.21745/) (2025)
- [Agent-based Automated Claim Matching with Instruction-following LLMs](https://mlsanity.com/paper/2510.23924/) (2025)
- [Use of Retrieval-Augmented Large Language Model Agent for Long-Form COVID-19 Fact-Checking](https://mlsanity.com/paper/2512.00007/) (2025)
- [Angry but Accurate: Detecting and Profiling the Counter-Misinformation Ecosystem on Twitter](https://mlsanity.com/paper/2607.02900/) (2026)
- [Detecting and Explaining Fake News Short Videos with Multimodal Content and Real-World Evidence](https://mlsanity.com/paper/2609.12678/) (2026)

## Abstract (arXiv)

Our society is facing rampant misinformation harming public health and trust. To address the societal challenge, we introduce FACT-GPT, a system leveraging Large Language Models (LLMs) to automate the claim matching stage of fact-checking. FACT-GPT, trained on a synthetic dataset, identifies social media content that aligns with, contradicts, or is irrelevant to previously debunked claims. Our evaluation shows that our specialized LLMs can match the accuracy of larger models in identifying related claims, closely mirroring human judgment. This research provides an automated solution for efficient claim matching, demonstrates the potential of LLMs in supporting fact-checkers, and offers valuable resources for further research in the field.