Summary of Kv Prediction For Improved Time to First Token, by Maxwell Horton et al.
KV Prediction for Improved Time to First Token
by Maxwell Horton, Qingqing Cao, Chenfan Sun, Yanzi Jin, Sachin Mehta, Mohammad Rastegari, Moin Nabi
First submitted to arxiv on: 10 Oct 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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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 proposes a novel method called KV Prediction to reduce the latency of transformer-based language models during the prompt processing step. The current approach can take tens of seconds or more for large models on edge devices, degrading user experience. The authors introduce an auxiliary model that predicts the KV cache needed by the base model, allowing for autoregressive generation without reprocessing the prompt. Experimental results demonstrate a pareto-optimal efficiency-accuracy trade-off, with relative accuracy improvements ranging from 15% to 50% on TriviaQA and up to 30% on HumanEval python code completion tasks at fixed latency budgets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about making language models faster. Right now, it takes a long time for them to start generating text after you give them a prompt. This slows down the whole process. To fix this, the authors came up with a new way of predicting what the model needs to know before it starts generating text. This lets the model generate text much faster without losing accuracy. The results show that this new method can make language models 15% to 50% more accurate and up to 30% faster on certain tasks. |
Keywords
» Artificial intelligence » Autoregressive » Prompt » Transformer
