Summary of C3ot: Generating Shorter Chain-of-thought Without Compromising Effectiveness, by Yu Kang et al.
C3oT: Generating Shorter Chain-of-Thought without Compromising Effectiveness
by Yu Kang, Xianghui Sun, Liangyu Chen, Wei Zou
First submitted to arxiv on: 16 Dec 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: 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 The paper introduces a new framework for compressing chain-of-thought (CoT) in large language models (LLMs), allowing for significant reductions in decoding costs and improved reasoning capabilities. The authors present C3oT, a novel compression approach that condenses longer CoTs into shorter ones while preserving key information and interpretability. This is achieved through a combination of a compressor, conditioned training methods, and conditioned inference techniques. Experimental results on four datasets demonstrate the effectiveness of the proposed method in compressing CoT lengths by up to 50% without sacrificing accuracy. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper creates a new way to help large language models think more efficiently. It shows that if we “shorten” the steps they take to come up with an answer, even if it’s just a little bit, their answers become less accurate. This is a problem because many applications need fast results, like search engines or recommendation systems. To solve this issue, the authors developed a method called C3oT that compresses long chains of thought into shorter ones while keeping them meaningful and easy to understand. They tested it on several datasets and found that it can cut the length of these chains by over 50% without losing accuracy. |
Keywords
» Artificial intelligence » Inference
