# Web Retrieval Agents for Evidence-Based Misinformation Detection

arXiv: https://arxiv.org/abs/2409.00009 · HTML: https://mlsanity.com/paper/2409.00009/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- Placed in Web, search, and research agents, one of 16 directions in LLM agents and tool use.
- Builds on 26 of its 40 references that are in the map.
- First posted on arXiv 15 Aug 2024.

## Placed in

- LLM agents and tool use / [Web, search, and research agents](https://mlsanity.com/agents/web-search-and-research-agents/)

## Builds on (most cited first, 26 of 26)

- [Chain-of-Thought Prompting Elicits Reasoning in Large Language Models](https://mlsanity.com/paper/2201.11903/) (2022)
- [ReAct: Synergizing Reasoning and Acting in Language Models](https://mlsanity.com/paper/2210.03629/) (2022)
- [HotpotQA: A Dataset for Diverse, Explainable Multi-hop Question Answering](https://mlsanity.com/paper/1809.09600/) (2018)
- [Retrieval-Augmented Generation for Large Language Models: A Survey](https://mlsanity.com/paper/2312.10997/) (2023)
- [FEVER: a large-scale dataset for Fact Extraction and VERification](https://mlsanity.com/paper/1803.05355/) (2018)
- [FActScore: Fine-grained Atomic Evaluation of Factual Precision in Long Form Text Generation](https://mlsanity.com/paper/2305.14251/) (2023)
- [Teaching Models to Express Their Uncertainty in Words](https://mlsanity.com/paper/2205.14334/) (2022)
- [ARES: An Automated Evaluation Framework for Retrieval-Augmented Generation Systems](https://mlsanity.com/paper/2311.09476/) (2023)
- [FacTool: Factuality Detection in Generative AI -- A Tool Augmented Framework for Multi-Task and Multi-Domain Scenarios](https://mlsanity.com/paper/2307.13528/) (2023)
- [ChatGPT-4 Outperforms Experts and Crowd Workers in Annotating Political Twitter Messages with Zero-Shot Learning](https://mlsanity.com/paper/2304.06588/) (2023)
- [Towards LLM-based Fact Verification on News Claims with a Hierarchical Step-by-Step Prompting Method](https://mlsanity.com/paper/2310.00305/) (2023)
- [Combating Misinformation in the Age of LLMs: Opportunities and Challenges](https://mlsanity.com/paper/2311.05656/) (2023)
- [X-FACT: A New Benchmark Dataset for Multilingual Fact Checking](https://mlsanity.com/paper/2106.09248/) (2021)
- [WikiChat: Stopping the Hallucination of Large Language Model Chatbots by Few-Shot Grounding on Wikipedia](https://mlsanity.com/paper/2305.14292/) (2023)
- [Towards Reliable Misinformation Mitigation: Generalization, Uncertainty, and GPT-4](https://mlsanity.com/paper/2305.14928/) (2023)
- [FactLLaMA: Optimizing Instruction-Following Language Models with External Knowledge for Automated Fact-Checking](https://mlsanity.com/paper/2309.00240/) (2023)
- [Explainable Claim Verification via Knowledge-Grounded Reasoning with Large Language Models](https://mlsanity.com/paper/2310.05253/) (2023)
- [FaVIQ: FAct Verification from Information-seeking Questions](https://mlsanity.com/paper/2107.02153/) (2021)
- [Open, Closed, or Small Language Models for Text Classification?](https://mlsanity.com/paper/2308.10092/) (2023)
- [A Review on Fact Extraction and Verification](https://mlsanity.com/paper/2010.03001/) (2020)
- [Combining Confidence Elicitation and Sample-based Methods for Uncertainty Quantification in Misinformation Mitigation](https://mlsanity.com/paper/2401.08694/) (2024)
- [Large Search Model: Redefining Search Stack in the Era of LLMs](https://mlsanity.com/paper/2310.14587/) (2023)
- [Causal Walk: Debiasing Multi-Hop Fact Verification with Front-Door Adjustment](https://mlsanity.com/paper/2403.02698/) (2024)
- [MUSER: A MUlti-Step Evidence Retrieval Enhancement Framework for Fake News Detection](https://mlsanity.com/paper/2306.13450/) (2023)
- [Is Explanation the Cure? Misinformation Mitigation in the Short Term and Long Term](https://mlsanity.com/paper/2310.17711/) (2023)
- [Uncertainty Resolution in Misinformation Detection](https://mlsanity.com/paper/2401.01197/) (2024)

## Built on by (most cited first, 13 of 13)

- [Evaluating LLM-based Agents for Multi-Turn Conversations: A Survey](https://mlsanity.com/paper/2503.22458/) (2025)
- [How to Protect Yourself from 5G Radiation? Investigating LLM Responses to Implicit Misinformation](https://mlsanity.com/paper/2503.09598/) (2025)
- [A Guide to Misinformation Detection Data and Evaluation](https://mlsanity.com/paper/2411.05060/) (2024)
- [All That Glisters Is Not Gold: A Benchmark for Reference-Free Counterfactual Financial Misinformation Detection](https://mlsanity.com/paper/2601.04160/) (2026)
- [CrediRAG: Network-Augmented Credibility-Based Retrieval for Misinformation Detection in Reddit](https://mlsanity.com/paper/2410.12061/) (2024)
- [Multi-Agent Retrieval-Augmented Framework for Evidence-Based Counterspeech Against Health Misinformation](https://mlsanity.com/paper/2507.07307/) (2025)
- [Source or It Didn't Happen: A Multi-Agent Framework for Citation Hallucination Detection](https://mlsanity.com/paper/2605.08583/) (2026)
- [Multi-agent Systems for Misinformation Lifecycle : Detection, Correction And Source Identification](https://mlsanity.com/paper/2505.17511/) (2025)
- [Veracity: An Open-Source AI Fact-Checking System](https://mlsanity.com/paper/2506.15794/) (2025)
- [ZoFia: Zero-Shot Fake News Detection with Entity-Guided Retrieval and Multi-LLM Interaction](https://mlsanity.com/paper/2511.01188/) (2025)
- [Exploring Health Misinformation Detection with Multi-Agent Debate](https://mlsanity.com/paper/2512.09935/) (2025)
- [TRUST Agents: A Collaborative Multi-Agent Framework for Fake News Detection, Explainable Verification, and Logic-Aware Claim Reasoning](https://mlsanity.com/paper/2604.12184/) (2026)
- [Large Language Models in Misinformation Ecosystems: Misuse, Defense, and Vulnerability](https://mlsanity.com/paper/2607.10402/) (2026)

## Abstract (arXiv)

This paper develops an agent-based automated fact-checking approach for detecting misinformation. We demonstrate that combining a powerful LLM agent, which does not have access to the internet for searches, with an online web search agent yields better results than when each tool is used independently. Our approach is robust across multiple models, outperforming alternatives and increasing the macro F1 of misinformation detection by as much as 20 percent compared to LLMs without search. We also conduct extensive analyses on the sources our system leverages and their biases, decisions in the construction of the system like the search tool and the knowledge base, the type of evidence needed and its impact on the results, and other parts of the overall process. By combining strong performance with in-depth understanding, we hope to provide building blocks for future search-enabled misinformation mitigation systems.