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