Summary of Faster Cascades Via Speculative Decoding, by Harikrishna Narasimhan et al.
Faster Cascades via Speculative Decoding
by Harikrishna Narasimhan, Wittawat Jitkrittum, Ankit Singh Rawat, Seungyeon Kim, Neha Gupta, Aditya Krishna Menon, Sanjiv Kumar
First submitted to arxiv on: 29 May 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 This paper proposes a new method for improving inference efficiency in language models by combining two existing approaches: cascades and speculative decoding. Cascades involve deferring to a larger model only for difficult inputs, while speculative decoding uses parallel verification mode to primarily invoke the larger model. The authors leverage the benefits of both methods by designing new “speculative cascading” techniques that implement their deferral rule through speculative execution. They characterize the optimal deferral rule and develop a plug-in approximation to it. Experimental results show that their approach outperforms baselines in terms of cost-quality trade-offs, using Gemma and T5 models on various language benchmarks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about making language models work better by combining two old ideas. One idea says use the bigger model only when you really need to, and the other idea says run everything in parallel to be sure it’s right. The authors took these ideas and mixed them together to create something new. They figured out how to do this in a way that makes sense and then tested it with some big models on lots of language tasks. It turns out their new method works really well and is better than the old ways at getting good results while using less computer power. |
Keywords
» Artificial intelligence » Inference » T5
