Summary of Leapformer: Enabling Linear Transformers For Autoregressive and Simultaneous Tasks Via Learned Proportions, by Victor Agostinelli et al.
LeaPformer: Enabling Linear Transformers for Autoregressive and Simultaneous Tasks via Learned Proportions
by Victor Agostinelli, Sanghyun Hong, Lizhong Chen
First submitted to arxiv on: 18 May 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Machine Learning (cs.LG)
GrooveSquid.com Paper Summaries
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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 In this paper, researchers propose a new approach to improving model performance in linearized transformers by using position-based re-weighting functions. The current state-of-the-art methods rely heavily on target sequence lengths, which makes it difficult to apply them to tasks with unknown or varying sequence lengths. To address this issue, the authors introduce Learned Proportions (LeaP) and LeaPformers, which generalize the dependence on explicit positional representations and sequence lengths into dependence on sequence proportions for re-weighting. The new approach replaces static positional representations with dynamic proportions derived via a compact module, enabling more flexible attention concentration patterns. The proposed method is evaluated against eight representative efficient transformers on the Long-Range Arena benchmark, showing that LeaPformer achieves the best quality-throughput trade-off. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper proposes a way to make models better at understanding and generating text by using a new type of re-weighting function in linearized transformers. The current methods are only good for tasks where we know how long the text is, but this new approach can work with any length. It does this by changing how it uses position-based information, so it’s more flexible and can handle different types of texts. |
Keywords
» Artificial intelligence » Attention
