Summary of Mep: Multiple Kernel Learning Enhancing Relative Positional Encoding Length Extrapolation, by Weiguo Gao
MEP: Multiple Kernel Learning Enhancing Relative Positional Encoding Length Extrapolation
by Weiguo Gao
First submitted to arxiv on: 26 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- 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 When the predicted sequence length exceeds the length seen during training, the transformer’s inference accuracy diminishes. This study proposes a novel relative positional encoding method, called MEP, which employs a weighted average to combine distinct kernel functions to generate a bias that is applied to post-softmax attention scores. The framework utilizes various kernel functions to construct multiple kernel functions, each with consistent mean weight coefficients and tailored slopes to enhance the model’s extrapolation capabilities. Two variants of this method are presented: a parameter-free variant that requires no new learnable parameters and a parameterized variant capable of integrating state-of-the-art techniques. Empirical evaluations across diverse datasets demonstrate that both variants achieve state-of-the-art performance, outperforming traditional approaches. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about finding a way to make transformers work better when the sequences are longer than they were during training. The current methods for doing this have some limitations and don’t take full advantage of different types of kernel functions. This study proposes a new method that uses a combination of these kernel functions, along with some clever calculations to create a bias that helps the transformer do a better job when it encounters long sequences. There are two versions of this method: one that doesn’t require any extra learning and another that does, but both can be used to improve the performance of transformers on long sequence tasks. |
Keywords
» Artificial intelligence » Attention » Inference » Positional encoding » Softmax » Transformer
