bibtext: @inproceedings{
  tian2024web,
  title={Web Retrieval Agents for Evidence-Based Misinformation Detection},
  author={Jacob-Junqi Tian and Hao Yu and Yury Orlovskiy and Tyler Vergho and Mauricio Rivera and Mayank Goel and Zachary Yang and Jean-Fran{\c{c}}ois Godbout and Reihaneh Rabbany and Kellin Pelrine},
  booktitle={First Conference on Language Modeling},
  year={2024},
  url={https://openreview.net/forum?id=pKMxO0wBYZ}
  }
abstract: 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.
title: Web Retrieval Agents for Evidence-Based Misinformation Detection
authors: Jacob-Junqi Tian Hao Yu Yury Orlovskiy Tyler Vergho Mauricio Rivera Mayank Goel Zachary Yang Jean-François Godbout Reihaneh Rabbany Kellin Pelrine