Summary of Claim Verification in the Age Of Large Language Models: a Survey, by Alphaeus Dmonte et al.
Claim Verification in the Age of Large Language Models: A Survey
by Alphaeus Dmonte, Roland Oruche, Marcos Zampieri, Prasad Calyam, Isabelle Augenstein
First submitted to arxiv on: 26 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 This survey presents a comprehensive account of recent claim verification frameworks using Large Language Models (LLMs). The paper describes the different components of the claim verification pipeline used in these frameworks, including common approaches to retrieval, prompting, and fine-tuning. The LLM-based approaches have shown superior performance in several NLP tasks, leading to a surge of interest in their application to claim verification. The survey covers publicly available English datasets created for this task. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper explores the development of automated claim verification systems using Large Language Models (LLMs) and other deep learning and transformer-based models. It presents a comprehensive account of recent claim verification frameworks that have shown superior performance in several NLP tasks. |
Keywords
» Artificial intelligence » Deep learning » Fine tuning » Nlp » Prompting » Transformer
