Summary of Exploring and Improving Drafts in Blockwise Parallel Decoding, by Taehyeon Kim et al.
Exploring and Improving Drafts in Blockwise Parallel Decoding
by Taehyeon Kim, Ananda Theertha Suresh, Kishore Papineni, Michael Riley, Sanjiv Kumar, Adrian Benton
First submitted to arxiv on: 14 Apr 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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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 investigates ways to improve the inference speed of autoregressive language models, particularly those using Blockwise Parallel Decoding (BPD). BPD aims to accelerate sequential token generation by predicting multiple future tokens simultaneously. The authors analyze the token distributions produced by multiple prediction heads and develop algorithms to refine block drafts using n-gram and neural language models. Experimental results show that refined block drafts lead to a 5-21% increase in block efficiency across various datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps improve autoregressive language models’ speed. They use an idea called Blockwise Parallel Decoding (BPD) which predicts multiple words at once. The authors look deeper into how BPD works and create new ways to make it better. They test these improvements on different datasets and find that they work well, making the language model faster. |
Keywords
» Artificial intelligence » Autoregressive » Inference » Language model » N gram » Token
