Evidence-backed Fact Checking using RAG and Few-Shot In-Context Learning
  with LLMs
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.
66c8e4f5121996313f3e515d
Ronit Singhal
66c8e4f5121996313f3e515e
Pransh Patwa
66c8e4f5121996313f3e515f
Parth Patwa
66c8e4f5121996313f3e5160
Aman Chadha
claimed_verified
2024-08-26T07:15:00.950Z
63a4754927f1f64ed7238dac
https://cdn-avatars.huggingface.co/v1/production/uploads/63a4754927f1f64ed7238dac/aH-eJF-31g4vof9jv2gmI.jpeg
Aman Chadha
user
amanchadha
66c8e4f5121996313f3e5161
Amitava Das
Evidence-backed Fact Checking using RAG and Few-Shot In-Context Learning
  with LLMs
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.
A Retrieve and Generate (RAG) pipeline combined with large language models (LLMs) improves automated fact-checking by extracting supporting evidence and classifying claim veracity.
Retrieve and Generate (RAG) pipeline large language models (LLMs) few-shot In-Context Learning (ICL)