ZoFia: Zero-Shot Fake News Detection with Entity-Guided Retrieval and Multi-LLM Interaction

ACL ARR 2026 January Submission10567 Authors

Keywords: Fake News Detection, Zero-Shot, Multi-Agent System, Information Retrieval, Entity Extraction

Abstract:

The rapid spread of fake news threatens social stability and public trust, highlighting the urgent need for its effective detection.
Although large language models (LLMs) show potential in fake news detection, they are limited by knowledge cutoff and easily generate factual hallucinations when handling time-sensitive news.
Furthermore, the thinking of a single LLM easily falls into early stance locking and confirmation bias, making it hard to handle both content reasoning and fact checking simultaneously.
To address these challenges, we propose ZoFia, a two-stage zero-shot fake news detection framework.
In the first retrieval stage, we propose novel Hierarchical Salience and Salience-Calibrated Minimum Marginal Relevance (SC-MMR) algorithm to extract core entities accurately, which drive dual-source retrieval to overcome knowledge and evidence gaps.
In the subsequent stage, a multi-agent system conducts multi-perspective reasoning and verification in parallel and achieve an explainable and robust result via adversarial debate.
Comprehensive experiments on two public datasets show that ZoFia outperforms existing zero-shot baselines and even most few-shot methods. Our code will be open-sourced to facilitate the related community.

Paper Type: Long

Research Area: Computational Social Science, Cultural Analytics, and NLP for Social Good

Research Area Keywords: Fake News Detection, Multi-Agent Systems, Open Information Extraction

Contribution Types: Model analysis & interpretability, NLP engineering experiment

Languages Studied: English

Submission Number: 10567