Summary of Expediting and Elevating Large Language Model Reasoning Via Hidden Chain-of-thought Decoding, by Tianqiao Liu et al.
Expediting and Elevating Large Language Model Reasoning via Hidden Chain-of-Thought Decoding
by Tianqiao Liu, Zui Chen, Zitao Liu, Mi Tian, Weiqi Luo
First submitted to arxiv on: 13 Sep 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 proposed novel approach compresses the chain-of-thought (CoT) process through semantic alignment, enabling more efficient decoding while preserving the benefits of CoT reasoning. The method introduces an auxiliary CoT model that learns to generate and compress the full thought process into a compact special token representation semantically aligned with the original CoT output. This compressed representation is then integrated into the input of the Hidden Chain-of-Thought (HCoT) model, achieving competitive or improved performance across three challenging domains while providing significant speedups in decoding time. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper proposes an innovative way to make large language models more efficient. It uses a special technique called semantic alignment to shrink down the thought process into a smaller version that can be used quickly and accurately. This means that these powerful AI models can now perform complex tasks much faster, which is really important for many applications. |
Keywords
» Artificial intelligence » Alignment » Token
