Summary of Faster Language Models with Better Multi-token Prediction Using Tensor Decomposition, by Artem Basharin et al.
Faster Language Models with Better Multi-Token Prediction Using Tensor Decomposition
by Artem Basharin, Andrei Chertkov, Ivan Oseledets
First submitted to arxiv on: 23 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 We present a novel transformer-based approach for multi-token prediction, focusing on efficient sampling without sacrificing accuracy. Building upon recent work that employs multiple heads to predict subsequent tokens, we establish connections with rank-1 canonical tensor decomposition. By generalizing this concept to rank-r canonical probability decomposition, our model predicts multiple tokens simultaneously, inheriting benefits from the mixture of experts framework for efficient and robust training. Our method exhibits notable speedups in inference for both text and code generation tasks within the self-speculative decoding paradigm, demonstrating robustness and scalability across various model sizes and training epochs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary We’ve developed a new way to predict multiple words at once using transformers. This helps us make predictions faster without sacrificing accuracy. Our approach is based on an idea from recent research that predicts the chances of different words coming next. We took this idea and expanded it to predict multiple words simultaneously. This lets us use techniques from another area of study called “mixture of experts” to improve training and prediction speed. Our method makes predictions much faster for tasks like writing text or code, especially when using a specific type of decoding. |
Keywords
» Artificial intelligence » Inference » Mixture of experts » Probability » Token » Transformer
