Summary of Evidence-backed Fact Checking Using Rag and Few-shot In-context Learning with Llms, by Ronit Singhal et al.
Evidence-backed Fact Checking using RAG and Few-Shot In-Context Learning with LLMs
by Ronit Singhal, Pransh Patwa, Parth Patwa, Aman Chadha, Amitava Das
First submitted to arxiv on: 22 Aug 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary In this paper, the authors develop an automated fact-checking system to combat misinformation on social media. They utilize the Averitec dataset and a Retrieve and Generate (RAG) pipeline to extract relevant evidence sentences from a knowledge base. These sentences are then inputted along with the claim into a large language model (LLM) for classification. The authors also evaluate the few-shot In-Context Learning (ICL) capabilities of multiple LLMs. Their system achieves an ‘Averitec’ score of 0.33, which is a 22% absolute improvement over the baseline. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This automated fact-checking system can help verify online claims and prevent misinformation from spreading. It uses the Averitec dataset to train a model that extracts relevant evidence sentences from a knowledge base. This information is then used to classify whether an online claim is true or false. The authors also test different language models to see which one works best. |
Keywords
» Artificial intelligence » Classification » Few shot » Knowledge base » Large language model » Rag
