Evidence-backed Fact Checking using RAG and Few-Shot In-Context Learning with LLMs
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| Main Authors: | , , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866914965161508864 |
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| author | Singhal, Ronit Patwa, Pransh Patwa, Parth Chadha, Aman Das, Amitava |
| author_facet | Singhal, Ronit Patwa, Pransh Patwa, Parth Chadha, Aman Das, Amitava |
| contents | 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-learning-with-llms. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2408_12060 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Evidence-backed Fact Checking using RAG and Few-Shot In-Context Learning with LLMs Singhal, Ronit Patwa, Pransh Patwa, Parth Chadha, Aman Das, Amitava Computation and Language Artificial Intelligence 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-learning-with-llms. |
| title | Evidence-backed Fact Checking using RAG and Few-Shot In-Context Learning with LLMs |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/2408.12060 |