Summary of Escape Sky-high Cost: Early-stopping Self-consistency For Multi-step Reasoning, by Yiwei Li et al.
Escape Sky-high Cost: Early-stopping Self-Consistency for Multi-step Reasoning
by Yiwei Li, Peiwen Yuan, Shaoxiong Feng, Boyuan Pan, Xinglin Wang, Bin Sun, Heda Wang, Kan Li
First submitted to arxiv on: 19 Jan 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 ESC (Early-Stopping Self-Consistency) method reduces the computational cost of self-consistent decoding in chain-of-thought reasoning tasks without sacrificing performance. By dynamically adjusting the sampling process, ESC outperforms traditional SC methods on six benchmark datasets, including arithmetic, commonsense, and symbolic reasoning over language models with varying scales. The results demonstrate a significant reduction in average sampling numbers, with improvements ranging from 33.8% to 84.2% compared to the original SC method. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper makes chain-of-thought reasoning more efficient by introducing ESC, a simple and scalable way to reduce computational costs without losing performance. It’s like finding a shortcut that works just as well! By dynamically adjusting the sampling process, ESC can be used on different tasks and models. The results show that it works really well on many types of problems, making it a useful tool for researchers and developers. |
Keywords
» Artificial intelligence » Early stopping
