Summary of Retrieval Augmented Fact Verification by Synthesizing Contrastive Arguments, By Zhenrui Yue et al.
Retrieval Augmented Fact Verification by Synthesizing Contrastive Arguments
by Zhenrui Yue, Huimin Zeng, Lanyu Shang, Yifan Liu, Yang Zhang, Dong Wang
First submitted to arxiv on: 14 Jun 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 The proposed retrieval augmented fact verification through synthesis of contrasting arguments (RAFTS) aims to combat misinformation by automatically verifying claim credibility. Existing methods rely heavily on embedded knowledge within large language models (LLMs) and/or black-box APIs for evidence collection, which can lead to subpar performance with smaller LLMs or unreliable context. RAFTS addresses these limitations by designing a retrieval pipeline to collect and re-rank relevant documents from verifiable sources, forming contrastive arguments conditioned on the retrieved evidence, and leveraging an embedding model to identify informative demonstrations. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The rapid spread of misinformation is a major problem that can harm public interest. To combat this issue, large language models are being used to automatically verify the credibility of claims. However, current methods rely too much on what these models already know or use black-box APIs for evidence collection, which can lead to poor results when using smaller models or unreliable information. This paper proposes a new approach called RAFTS (retrieval augmented fact verification through synthesis of contrasting arguments) that collects and re-ranks relevant documents from trusted sources, creates arguments for and against the claim, and uses an embedding model to identify important points. |
Keywords
» Artificial intelligence » Embedding
