Evidence-based Interpretable Open-domain Fact-checking with Large
  Language Models
Xin Tan Bowei Zou Ai Ti Aw
Universal fact-checking systems for real-world claims face significant
challenges in gathering valid and sufficient real-time evidence and making
reasoned decisions. In this work, we introduce the Open-domain Explainable
Fact-checking (OE-Fact) system for claim-checking in real-world scenarios. The
OE-Fact system can leverage the powerful understanding and reasoning
capabilities of large language models (LLMs) to validate claims and generate
causal explanations for fact-checking decisions. To adapt the traditional
three-module fact-checking framework to the open domain setting, we first
retrieve claim-related information as relevant evidence from open websites.
After that, we retain the evidence relevant to the claim through LLM and
similarity calculation for subsequent verification. We evaluate the performance
of our adapted three-module OE-Fact system on the Fact Extraction and
Verification (FEVER) dataset. Experimental results show that our OE-Fact system
outperforms general fact-checking baseline systems in both closed- and
open-domain scenarios, ensuring stable and accurate verdicts while providing
concise and convincing real-time explanations for fact-checking decisions.