Evidence-backed Fact Checking using RAG and Few-Shot In-Context Learning
  with LLMs
Authors: name: Ronit Singhal; name: Pransh Patwa; name: Parth Patwa; name: Aman Chadha; name: Amitava Das
Published: 2024-08-22 01:42:34

Abstract: Given the widespread dissemination of misinformation on social media,
implementing fact-checking mechanisms for online claims is essential. Manually
verifying every claim is highly 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 to assess the veracity of
claims. 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. All code will be
made available on All code will be made available on
https://github.com/ronit-singhal/evidence-backed-fact-checking-using-rag-and-few-shot-in-context-learning-with-llms.