# Evidence-backed Fact Checking using RAG and Few-Shot In-Context Learning with LLMs

arXiv: https://arxiv.org/abs/2408.12060 · HTML: https://mlsanity.com/paper/2408.12060/

## Key facts

- Cited by 24 papers in the map (data through 14 Oct 2026).
- Builds on 23 of its 45 references that are in the map.
- First posted on arXiv 22 Aug 2024.

## Builds on (most cited first, 23 of 23)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [The Llama 3 Herd of Models](https://mlsanity.com/paper/2407.21783/) (2024)
- [Qwen2 Technical Report](https://mlsanity.com/paper/2407.10671/) (2024)
- [BLOOM: A 176B-Parameter Open-Access Multilingual Language Model](https://mlsanity.com/paper/2211.05100/) (2022)
- [Phi-3 Technical Report: A Highly Capable Language Model Locally on Your Phone](https://mlsanity.com/paper/2404.14219/) (2024)
- [FEVER: a large-scale dataset for Fact Extraction and VERification](https://mlsanity.com/paper/1803.05355/) (2018)
- [Few-Shot Parameter-Efficient Fine-Tuning is Better and Cheaper than In-Context Learning](https://mlsanity.com/paper/2205.05638/) (2022)
- [Reading Wikipedia to Answer Open-Domain Questions](https://mlsanity.com/paper/1704.00051/) (2017)
- [A Survey on Automated Fact-Checking](https://mlsanity.com/paper/2108.11896/) (2021)
- [Get Your Vitamin C! Robust Fact Verification with Contrastive Evidence](https://mlsanity.com/paper/2103.08541/) (2021)
- [AVeriTeC: A Dataset for Real-world Claim Verification with Evidence from the Web](https://mlsanity.com/paper/2305.13117/) (2023)
- [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)
- [Fighting an Infodemic: COVID-19 Fake News Dataset](https://mlsanity.com/paper/2011.03327/) (2020)
- [Explainable Automated Fact-Checking: A Survey](https://mlsanity.com/paper/2011.03870/) (2020)
- [ProoFVer: Natural Logic Theorem Proving for Fact Verification](https://mlsanity.com/paper/2108.11357/) (2021)
- [Can LLMs Produce Faithful Explanations For Fact-checking? Towards Faithful Explainable Fact-Checking via Multi-Agent Debate](https://mlsanity.com/paper/2402.07401/) (2024)
- [Integrating Stance Detection and Fact Checking in a Unified Corpus](https://mlsanity.com/paper/1804.08012/) (2018)
- [Factify 2: A Multimodal Fake News and Satire News Dataset](https://mlsanity.com/paper/2304.03897/) (2023)
- [CrAM: Credibility-Aware Attention Modification in LLMs for Combating Misinformation in RAG](https://mlsanity.com/paper/2406.11497/) (2024)
- [g2tmn at Constraint@AAAI2021: Exploiting CT-BERT and Ensembling Learning for COVID-19 Fake News Detection](https://mlsanity.com/paper/2012.11967/) (2020)
- [Enhancing Low-Resource LLMs Classification with PEFT and Synthetic Data](https://mlsanity.com/paper/2404.02422/) (2024)
- [COVID-19 Tests Gone Rogue: Privacy, Efficacy, Mismanagement and Misunderstandings](https://mlsanity.com/paper/2101.01693/) (2021)

## Built on by (most cited first, 24 of 24)

- [The Automated Verification of Textual Claims (AVeriTeC) Shared Task](https://mlsanity.com/paper/2410.23850/) (2024)
- [Hallucination to Truth: A Review of Fact-Checking and Factuality Evaluation in Large Language Models](https://mlsanity.com/paper/2508.03860/) (2025)
- [A Survey of Scaling in Large Language Model Reasoning](https://mlsanity.com/paper/2504.02181/) (2025)
- [The Next Phase of Scientific Fact-Checking: Advanced Evidence Retrieval from Complex Structured Academic Papers](https://mlsanity.com/paper/2506.20844/) (2025)
- [Towards Geo-Culturally Grounded LLM Generations](https://mlsanity.com/paper/2502.13497/) (2025)
- [HintsOfTruth: A Multimodal Checkworthiness Detection Dataset with Real and Synthetic Claims](https://mlsanity.com/paper/2502.11753/) (2025)
- [Uncovering the Bigger Picture: Comprehensive Event Understanding Via Diverse News Retrieval](https://mlsanity.com/paper/2508.19758/) (2025)
- [Task-Oriented Automatic Fact-Checking with Frame-Semantics](https://mlsanity.com/paper/2501.13288/) (2025)
- [EMULATE: A Multi-Agent Framework for Determining the Veracity of Atomic Claims by Emulating Human Actions](https://mlsanity.com/paper/2505.16576/) (2025)
- [A Comprehensive Dataset for Human vs. AI Generated Text Detection](https://mlsanity.com/paper/2510.22874/) (2025)
- [SocialDF: Benchmark Dataset and Detection Model for Mitigating Harmful Deepfake Content on Social Media Platforms](https://mlsanity.com/paper/2506.05538/) (2025)
- [Fact in Fragments: Deconstructing Complex Claims via LLM-based Atomic Fact Extraction and Verification](https://mlsanity.com/paper/2506.07446/) (2025)
- [RAG over Thinking Traces Can Improve Reasoning Tasks](https://mlsanity.com/paper/2605.03344/) (2026)
- [Shared Nature, Unique Nurture: PRISM for Pluralistic Reasoning via In-context Structure Modeling](https://mlsanity.com/paper/2602.21317/) (2026)
- [Automatic Fact-checking in English and Telugu](https://mlsanity.com/paper/2509.26415/) (2025)
- [Use of Retrieval-Augmented Large Language Model Agent for Long-Form COVID-19 Fact-Checking](https://mlsanity.com/paper/2512.00007/) (2025)
- [Frame-Guided Synthetic Claim Generation for Automatic Fact-Checking Using High-Volume Tabular Data](https://mlsanity.com/paper/2601.17232/) (2026)
- [Agentic Adversarial Rewriting Exposes Architectural Vulnerabilities in Black-Box NLP Pipelines](https://mlsanity.com/paper/2604.23483/) (2026)
- [The Periodic Table of LLM Reasoning: A Structured Survey of Reasoning Paradigms, Methods, and Failure Modes](https://mlsanity.com/paper/2606.11470/) (2026)
- [Generating Biomedical Fact-Checking Reports with RL-Enhanced Agentic Search](https://mlsanity.com/paper/2608.23811/) (2026)
- [Detecting and Explaining Fake News Short Videos with Multimodal Content and Real-World Evidence](https://mlsanity.com/paper/2609.12678/) (2026)
- [Medical Knowledge Simplification for Patients in the Era of LLMs: A Case Study on Diabetes](https://mlsanity.com/paper/2609.15129/) (2026)
- [EvidenT: Building Trustworthy Enterprise Assistants through Evidence Groundedness and Traceability](https://mlsanity.com/paper/2609.22537/) (2026)
- [Is the Future Predictable? Modeling Future Predictability for Reliable Temporal Reasoning](https://mlsanity.com/paper/2610.14073/) (2026)

## Abstract (arXiv)

Given the widespread dissemination of misinformation on social media, implementing fact-checking mechanisms for online claims is essential. Manually verifying every claim is very challenging, underscoring the need for an automated fact-checking system. This paper presents our system designed to address this issue. We utilize the Averitec dataset (Schlichtkrull et al., 2023) to assess the performance of our fact-checking system. In addition to veracity prediction, our system provides supporting evidence, which is extracted from the dataset. We develop a Retrieve and Generate (RAG) pipeline to extract relevant evidence sentences from a knowledge base, which are then inputted along with the claim into a large language model (LLM) for classification. We also evaluate the few-shot In-Context Learning (ICL) capabilities of multiple LLMs. Our system achieves an 'Averitec' score of 0.33, which is a 22% absolute improvement over the baseline. Our Code is publicly available on https://github.com/ronit-singhal/evidence-backed-fact-checking-using-rag-and-few-shot-in-context-learning-with-llms.