Summary of Cerberus: Efficient Inference with Adaptive Parallel Decoding and Sequential Knowledge Enhancement, by Yuxuan Liu et al.
Cerberus: Efficient Inference with Adaptive Parallel Decoding and Sequential Knowledge Enhancement
by Yuxuan Liu, Wenyuan Li, Laizhong Cui, Hailiang Yang
First submitted to arxiv on: 17 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 Medium Difficulty summary: This paper addresses the inference speed bottleneck in large language models (LLMs) by proposing Cerberus, an adaptive parallel decoding framework that balances prediction accuracy and execution parallelism. Cerberus introduces a gating mechanism to dynamically choose between auto-regressive and parallel decoding approaches at each step, as well as novel decoding heads that incorporate sequential knowledge while maintaining parallel execution. The experiment results show that Cerberus achieves up to 2.12x speedup compared to auto-regressive decoding, outperforming the leading parallel decoding framework Medusa with a 10% – 30% increase in acceleration and superior generation quality. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Low Difficulty summary: This research paper aims to make language models work faster without sacrificing their ability to generate good text. The team identified some problems with current methods that speed up inference, specifically issues with balancing accuracy and efficiency. To solve these problems, they created a new approach called Cerberus, which can adaptively choose the best way to decode at each step while maintaining parallel execution. The results show that Cerberus is much faster than previous methods, achieving speeds up to 2.12 times faster, and generates better text quality. |
Keywords
» Artificial intelligence » Inference
