Summary of Linear Transformers with Learnable Kernel Functions Are Better In-context Models, by Yaroslav Aksenov et al.
Linear Transformers with Learnable Kernel Functions are Better In-Context Models
by Yaroslav Aksenov, Nikita Balagansky, Sofia Maria Lo Cicero Vaina, Boris Shaposhnikov, Alexey Gorbatovski, Daniil Gavrilov
First submitted to arxiv on: 16 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computation and Language (cs.CL)
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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 As researchers continue to push the boundaries of natural language processing, it is essential to develop subquadratic architectures for Language Models (LMs) that can efficiently handle large-scale language understanding tasks. In recent years, State Space Models have been touted as a game-changer, surpassing Transformer performance on some language modeling tasks. However, these models have shown limitations in their ability to learn from context, an area where the Transformer traditionally excels. The Based model has emerged as a hybrid solution that combines a Linear Transformer with a kernel inspired by Taylor expansion and convolutional networks, demonstrating strong in-context learning capabilities. Our work presents a novel alteration to the Based kernel that enhances its In-Context Learning abilities on tasks such as Multi-Query Associative Recall and language modeling, evaluated on the Pile dataset. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Language models are important for understanding human communication. Researchers have been working on new ways to make language models faster and better. Some recent ideas, like State Space Models, were promising but had some limitations. The Based model is a combination of different ideas that works well in certain situations. Our new idea builds on the Based model and makes it even better at learning from context. |
Keywords
* Artificial intelligence * Language understanding * Natural language processing * Recall * Transformer
