Summary of E2e-afg: An End-to-end Model with Adaptive Filtering For Retrieval-augmented Generation, by Yun Jiang et al.
E2E-AFG: An End-to-End Model with Adaptive Filtering for Retrieval-Augmented Generation
by Yun Jiang, Zilong Xie, Wei Zhang, Yun Fang, Shuai Pan
First submitted to arxiv on: 1 Nov 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 an end-to-end model called E2E-AFG for retrieval-augmented generation. The model integrates answer existence judgment and text generation into a single framework to focus on relevant content and reduce misinformation. It outperforms baseline models on six knowledge-intensive language datasets, demonstrating its effectiveness and robustness. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper addresses a problem with current retrieval-augmented generation methods that neglect the quality of retrieved information. This can lead to irrelevant or inaccurate results. The proposed E2E-AFG model improves upon this by combining answer existence judgment and text generation in one step. This helps the model focus on relevant content and generate more accurate answers. |
Keywords
» Artificial intelligence » Retrieval augmented generation » Text generation
