Summary of Qpaug: Question and Passage Augmentation For Open-domain Question Answering Of Llms, by Minsang Kim et al.
QPaug: Question and Passage Augmentation for Open-Domain Question Answering of LLMs
by Minsang Kim, Cheoneum Park, Seungjun Baek
First submitted to arxiv on: 20 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 This paper proposes a novel approach called question and passage augmentation (QPaug) for open-domain question-answering tasks. By decomposing questions into sub-questions, QPaug improves retrieval performance by making the query more specific about what needs to be retrieved. Additionally, the method augments retrieved passages with self-generated passages from large language models to guide answer extraction. Experimental results show that QPaug outperforms previous state-of-the-art methods and achieves significant performance gains. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us get better answers by asking better questions! It’s like using a super smart librarian to help you find the right book. The researchers came up with a clever way to break down big questions into smaller, more focused ones. This makes it easier for computers to find the right information and give accurate answers. They also added some extra “help” passages to make sure the computer doesn’t get distracted or confused. With this new approach, we can get even better results! |
Keywords
» Artificial intelligence » Question answering
