# Retrieval Augmented Fact Verification by Synthesizing Contrastive Arguments

arXiv: https://arxiv.org/abs/2406.09815 · HTML: https://mlsanity.com/paper/2406.09815/

## Key facts

- Cited by 25 papers in the map (data through 14 Oct 2026).
- Placed in RAG robustness and adaptive retrieval, one of 15 directions in Retrieval-augmented generation.
- Builds on 49 of its 87 references that are in the map.
- First posted on arXiv 14 Jun 2024.

## Placed in

- Retrieval-augmented generation / [RAG robustness and adaptive retrieval](https://mlsanity.com/rag/rag-robustness-and-adaptive-retrieval/)

## Builds on (most cited first, 30 of 49)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Training language models to follow instructions with human feedback](https://mlsanity.com/paper/2203.02155/) (2022)
- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Llama 2: Open Foundation and Fine-Tuned Chat Models](https://mlsanity.com/paper/2307.09288/) (2023)
- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (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)
- [PaLM: Scaling Language Modeling with Pathways](https://mlsanity.com/paper/2204.02311/) (2022)
- [Dense Passage Retrieval for Open-Domain Question Answering](https://mlsanity.com/paper/2004.04906/) (2020)
- [Finetuned Language Models Are Zero-Shot Learners](https://mlsanity.com/paper/2109.01652/) (2021)
- [SimCSE: Simple Contrastive Learning of Sentence Embeddings](https://mlsanity.com/paper/2104.08821/) (2021)
- [REALM: Retrieval-Augmented Language Model Pre-Training](https://mlsanity.com/paper/2002.08909/) (2020)
- [Self-RAG: Learning to Retrieve, Generate, and Critique through Self-Reflection](https://mlsanity.com/paper/2310.11511/) (2023)
- [MS MARCO: A Human Generated MAchine Reading COmprehension Dataset](https://mlsanity.com/paper/1611.09268/) (2016)
- [Text Embeddings by Weakly-Supervised Contrastive Pre-training](https://mlsanity.com/paper/2212.03533/) (2022)
- [Leveraging Passage Retrieval with Generative Models for Open Domain Question Answering](https://mlsanity.com/paper/2007.01282/) (2020)
- [What Makes Good In-Context Examples for GPT-3?](https://mlsanity.com/paper/2101.06804/) (2021)
- [Atlas: Few-shot Learning with Retrieval Augmented Language Models](https://mlsanity.com/paper/2208.03299/) (2022)
- [REPLUG: Retrieval-Augmented Black-Box Language Models](https://mlsanity.com/paper/2301.12652/) (2023)
- [The RefinedWeb Dataset for Falcon LLM: Outperforming Curated Corpora with Web Data, and Web Data Only](https://mlsanity.com/paper/2306.01116/) (2023)
- [In-Context Retrieval-Augmented Language Models](https://mlsanity.com/paper/2302.00083/) (2023)
- ["Liar, Liar Pants on Fire": A New Benchmark Dataset for Fake News Detection](https://mlsanity.com/paper/1705.00648/) (2017)
- [Check Your Facts and Try Again: Improving Large Language Models with External Knowledge and Automated Feedback](https://mlsanity.com/paper/2302.12813/) (2023)
- [Fake News Detection on Social Media: A Data Mining Perspective](https://mlsanity.com/paper/1708.01967/) (2017)
- [Explainable Automated Fact-Checking for Public Health Claims](https://mlsanity.com/paper/2010.09926/) (2020)
- [Active Example Selection for In-Context Learning](https://mlsanity.com/paper/2211.04486/) (2022)
- [CORD-19: The COVID-19 Open Research Dataset](https://mlsanity.com/paper/2004.10706/) (2020)
- [Can LLM-Generated Misinformation Be Detected?](https://mlsanity.com/paper/2309.13788/) (2023)
- [Automated Fact-Checking for Assisting Human Fact-Checkers](https://mlsanity.com/paper/2103.07769/) (2021)

## Built on by (most cited first, 25 of 25)

- [Iterative Self-Incentivization Empowers Large Language Models as Agentic Searchers](https://mlsanity.com/paper/2505.20128/) (2025)
- [VeriFastScore: Speeding up long-form factuality evaluation](https://mlsanity.com/paper/2505.16973/) (2025)
- [Retrieval- and Argumentation-Enhanced Multi-Agent LLMs for Judgmental Forecasting (Extended Version with Supplementary Material)](https://mlsanity.com/paper/2510.24303/) (2025)
- [Debating Truth: Debate-driven Claim Verification with Multiple Large Language Model Agents](https://mlsanity.com/paper/2507.19090/) (2025)
- [Speaking at the Right Level: Literacy-Controlled Counterspeech Generation with RAG-RL](https://mlsanity.com/paper/2509.01058/) (2025)
- [Reasoning About the Unsaid: Misinformation Detection with Omission-Aware Graph Inference](https://mlsanity.com/paper/2512.01728/) (2025)
- [PASS-FC: Progressive and Adaptive Search Scheme for Fact Checking of Comprehensive Claims](https://mlsanity.com/paper/2504.09866/) (2025)
- [Fact in Fragments: Deconstructing Complex Claims via LLM-based Atomic Fact Extraction and Verification](https://mlsanity.com/paper/2506.07446/) (2025)
- [ClaimIQ at CheckThat! 2025: Comparing Prompted and Fine-Tuned Language Models for Verifying Numerical Claims](https://mlsanity.com/paper/2509.11492/) (2025)
- [FarExStance: Explainable Stance Detection for Farsi](https://mlsanity.com/paper/2412.14008/) (2024)
- [Entailed Opinion Matters: Improving the Fact-Checking Performance of Language Models by Relying on their Entailment Ability](https://mlsanity.com/paper/2505.15050/) (2025)
- [Retrieval-Augmented Generation Must Move Beyond Factual Grounding to Represent Diverse Opinions](https://mlsanity.com/paper/2604.12138/) (2026)
- [Debating the Unspoken: Role-Anchored Multi-Agent Reasoning for Half-Truth Detection](https://mlsanity.com/paper/2604.19005/) (2026)
- [AMATA: Adaptive Multi-Agent Trajectory Alignment for Knowledge-Intensive Question Answering](https://mlsanity.com/paper/2605.17352/) (2026)
- [DeepWeb-Bench: A Deep Research Benchmark Demanding Massive Cross-Source Evidence and Long-Horizon Derivation](https://mlsanity.com/paper/2605.21482/) (2026)
- [Automatic Fact-checking in English and Telugu](https://mlsanity.com/paper/2509.26415/) (2025)
- [Embedding-Based Context-Aware Reranker](https://mlsanity.com/paper/2510.13329/) (2025)
- [When retrieval outperforms generation: Dense evidence retrieval for scalable fake news detection](https://mlsanity.com/paper/2511.04643/) (2025)
- [MEVER: Multi-Modal and Explainable Claim Verification with Graph-based Evidence Retrieval](https://mlsanity.com/paper/2602.10023/) (2026)
- [A Graph-Enhanced Defense Framework for Explainable Fake News Detection with LLM](https://mlsanity.com/paper/2604.06666/) (2026)
- [Latent Causal Void: Explicit Missing-Context Reconstruction for Misinformation Detection](https://mlsanity.com/paper/2605.12156/) (2026)
- [CAAFC: Chronological Actionable Automated Fact-Checker for misinformation / non-factual hallucination detection and correction](https://mlsanity.com/paper/2605.12436/) (2026)
- [Mask-to-Correct^+: Leveraging Retriever Diversity for Masking-guided Faithful Fact Correction](https://mlsanity.com/paper/2605.18776/) (2026)
- [DN-Hypo-Pipeline: An AI-Driven Workflow for Generating Hypotheses using Large Language Models and Scientific Explanations](https://mlsanity.com/paper/2606.08532/) (2026)
- [GPE: Evaluating Robust Evidence Aggregation for Fact Verification under Controllable GEO-Style Poisoning](https://mlsanity.com/paper/2607.20730/) (2026)

## Abstract (arXiv)

The rapid propagation of misinformation poses substantial risks to public interest. To combat misinformation, large language models (LLMs) are adapted to automatically verify claim credibility. Nevertheless, existing methods heavily rely on the embedded knowledge within LLMs and / or black-box APIs for evidence collection, leading to subpar performance with smaller LLMs or upon unreliable context. In this paper, we propose retrieval augmented fact verification through the synthesis of contrasting arguments (RAFTS). Upon input claims, RAFTS starts with evidence retrieval, where we design a retrieval pipeline to collect and re-rank relevant documents from verifiable sources. Then, RAFTS forms contrastive arguments (i.e., supporting or refuting) conditioned on the retrieved evidence. In addition, RAFTS leverages an embedding model to identify informative demonstrations, followed by in-context prompting to generate the prediction and explanation. Our method effectively retrieves relevant documents as evidence and evaluates arguments from varying perspectives, incorporating nuanced information for fine-grained decision-making. Combined with informative in-context examples as prior, RAFTS achieves significant improvements to supervised and LLM baselines without complex prompts. We demonstrate the effectiveness of our method through extensive experiments, where RAFTS can outperform GPT-based methods with a significantly smaller 7B LLM.