Summary of Beyond Autoregression: Fast Llms Via Self-distillation Through Time, by Justin Deschenaux et al.
Beyond Autoregression: Fast LLMs via Self-Distillation Through Time
by Justin Deschenaux, Caglar Gulcehre
First submitted to arxiv on: 28 Oct 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 The paper explores the limitations of Autoregressive (AR) Large Language Models (LLMs) in generating text and introduces a novel distillation method for discrete diffusion models that enables simultaneous generation of at least 32 tokens. This approach outperforms AR models on the LAMBADA natural language understanding benchmark while reducing inference steps by a factor of 32-64. The authors also demonstrate the efficacy of their approach for diffusion language models with up to 860M parameters, which can generate tokens at a rate up to 8 times faster than AR models employing KV-caching. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about improving how computers understand and generate human-like text. Currently, most computer models can only write one sentence or word at a time, which takes a long time. New ideas from recent research show that we can make these models work better by using more powerful computers during the writing process. The authors of this study found a way to make their model write 32 words at once without losing quality. This is faster than other methods and works well even when they don’t use extra memory. |
Keywords
» Artificial intelligence » Autoregressive » Diffusion » Distillation » Inference » Language understanding
