Summary of Qaea-dr: a Unified Text Augmentation Framework For Dense Retrieval, by Hongming Tan et al.
QAEA-DR: A Unified Text Augmentation Framework for Dense Retrieval
by Hongming Tan, Shaoxiong Zhan, Hai Lin, Hai-Tao Zheng, Wai Kin Chan
First submitted to arxiv on: 29 Jul 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Information Retrieval (cs.IR)
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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 A novel text augmentation framework is introduced for dense retrieval, transforming raw documents into information-dense formats to address issues like information loss and noisy texts. The approach, called QAEA-DR, generates two text representations via large language models (LLMs) zero-shot prompting: question-answer pairs and element-driven events. This model enhances dense retrieval quality through a scoring-based evaluation and regeneration mechanism. Empirical experiments support the positive impact of QAEA-DR on dense retrieval. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Dense retrieval is a way to search for text without using keywords. But sometimes, important details get lost in translation. To fix this, researchers created a new method that takes raw texts and makes them more valuable by adding extra information. This helps when searching for relevant answers. The new approach uses big language models to create two types of texts: question-answer pairs and event-based stories. It’s called QAEA-DR. Tests show it improves search results. |
Keywords
» Artificial intelligence » Prompting » Translation » Zero shot
