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Evidence-Based Fact Checking with Averitec by hridya4research is a document available to read on EtoBox.

This paper presents an automated fact-checking system that utilizes a Retrieve and Generate (RAG) pipeline combined with Few-Shot In-Context Learning (ICL) using large language models (LLMs) to classify claims as Supported, Refuted, Conflicting Evidence, or Not Enough Evidence. The system retrieves relevant documents, extracts evidence, and predicts veracity, achieving a significant improvement over baseline performance on the Averitec dataset. The approach aims to enhance transparency and public trust in a

Author

hridya4research

Language

EN