Summary of Cllms: Consistency Large Language Models, by Siqi Kou et al.
CLLMs: Consistency Large Language Models
by Siqi Kou, Lanxiang Hu, Zhezhi He, Zhijie Deng, Hao Zhang
First submitted to arxiv on: 28 Feb 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 introduces a new approach to improve the efficiency of large language model (LLM) inference by developing a parallel decoding method that leverages Jacobi decoding’s potential for more efficient computation. The proposed method aims to accelerate convergence on a Jacobi trajectory, allowing for faster generation speed while preserving quality across domain-specific and open-domain benchmarks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper shows promise in improving the efficiency of LLM inference by breaking down the sequential nature of traditional autoregressive (AR) decoding and transforming it into parallelizable computation. The new approach targets fast convergence from any state to the fixed point on a Jacobi trajectory, refining the target LLM to consistently predict the fixed point given any state as input. |
Keywords
» Artificial intelligence » Autoregressive » Inference » Large language model
