Evidence-backed Fact Checking using RAG and Few-Shot In-Context Learning with LLMs

• URL: http://arxiv.org/abs/2408.12060v2
• Date: Fri, 4 Oct 2024 19:31:41 GMT
• Title: Evidence-backed Fact Checking using RAG and Few-Shot In-Context Learning with LLMs
• Authors: Ronit Singhal, Pransh Patwa, Parth Patwa, Aman Chadha, Amitava Das,
• Abstract summary: We use the Averitec dataset to assess the performance of our fact-checking system. In addition to veracity prediction, our system provides supporting evidence, which is extracted from the dataset. Our system achieves an 'Averitec' score of 0.33, which is a 22% absolute improvement over the baseline.
• Score: 9.785096589765908
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Given the widespread dissemination of misinformation on social media, implementing fact-checking mechanisms for online claims is essential. Manually verifying every claim is very challenging, underscoring the need for an automated fact-checking system. This paper presents our system designed to address this issue. We utilize the Averitec dataset (Schlichtkrull et al., 2023) to assess the performance of our fact-checking system. In addition to veracity prediction, our system provides supporting evidence, which is extracted from the dataset. We develop a Retrieve and Generate (RAG) pipeline to extract relevant evidence sentences from a knowledge base, which are then inputted along with the claim into a large language model (LLM) for classification. We also evaluate the few-shot In-Context Learning (ICL) capabilities of multiple LLMs. Our system achieves an 'Averitec' score of 0.33, which is a 22% absolute improvement over the baseline. Our Code is publicly available on https://github.com/ronit-singhal/evidence-backed-fact-checking-using-rag-and-few-shot-in-context-lea rning-with-llms.

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