Tuning LLMs for Explainable Misinformation Detection
Researchers propose a pipeline to fine-tune a dedicated Large Language Model (LLM) for explainable misinformation detection. The approach involves collecting large-scale fact-checked articles, using multiple strong LLMs to generate veracity predictions and rationales, and applying a filtering strategy to ensure high-quality training data. This work addresses the challenge of misinformation spread on social media by moving beyond traditional black-box binary classification toward transparent, rationale-based explanations. The study is published on arXiv under paper ID 2605.19285.
Key facts
- arXiv paper ID 2605.19285
- Proposes pipeline for fine-tuning LLM for explainable misinformation detection
- Collects large-scale fact-checked articles
- Uses multiple strong LLMs to generate predictions and rationales
- Applies filtering strategy for high-quality training data
- Addresses misinformation on social media
- Enhances transparency over black-box binary classification
- Published as arXiv preprint
Entities
Institutions
- arXiv