Summary of Refusion: Improving Natural Language Understanding with Computation-efficient Retrieval Representation Fusion, by Shangyu Wu et al.
ReFusion: Improving Natural Language Understanding with Computation-Efficient Retrieval Representation Fusion
by Shangyu Wu, Ying Xiong, Yufei Cui, Xue Liu, Buzhou Tang, Tei-Wei Kuo, Chun Jason Xue
First submitted to arxiv on: 4 Jan 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 research paper proposes a novel approach to retrieval-based augmentations (RA) for non-knowledge-intensive (NKI) tasks, which have historically been challenging to tackle using existing methods. The authors introduce ReFusion, a computation-efficient method that fuses retrieval representations directly into the hidden states of language models. This differs from previous approaches that concatenated retrievals with inputs, leading to increased input length and computational demands on attention mechanisms. The proposed bi-level optimization-based method leverages an adaptive retrieval integrator to optimize ranking schemes across model layers. Experimental results demonstrate superior and robust performance in various NKI tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary ReFusion is a new way to make language models better at understanding and generating text. Right now, researchers are using something called “retrieval-based augmentations” to help models learn more about certain topics. But this method only works well when the topic is very specific and technical, like science or history. The problem is that it doesn’t work as well for everyday conversations or texts that aren’t super detailed. This new approach, ReFusion, tries to fix this by taking the information from an external database and fusing it directly into the model’s hidden states. This makes the model better at understanding and generating text about everyday topics. |
Keywords
» Artificial intelligence » Attention » Optimization
