Summary of Crest: Effectively Compacting a Datastore For Retrieval-based Speculative Decoding, by Sophia Ho et al.
CREST: Effectively Compacting a Datastore For Retrieval-Based Speculative Decoding
by Sophia Ho, Jinsol Park, Patrick Wang
First submitted to arxiv on: 8 Aug 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Databases (cs.DB)
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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 A novel redesign of the Retrieval-Based Speculative Decoding (REST) technique, dubbed CREST, is proposed to achieve compact storage while maintaining performance. By storing only a subset of the smallest and most common n-gram matches in a datastore, CREST successfully reduces storage space by 10.6-13.5 times while achieving a higher acceptance length on HumanEval and MT Bench benchmarks compared to REST. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine you’re trying to generate text based on what someone else has written. This paper is about making that process more efficient by storing only the most important pieces of information needed to make predictions. By using less storage space, the system can still generate high-quality text while taking up less room. This matters because it could help us use AI for things like writing articles or creating chatbots. |
Keywords
» Artificial intelligence » N gram
