Summary of Make Every Penny Count: Difficulty-adaptive Self-consistency For Cost-efficient Reasoning, by Xinglin Wang et al.
Make Every Penny Count: Difficulty-Adaptive Self-Consistency for Cost-Efficient Reasoning
by Xinglin Wang, Shaoxiong Feng, Yiwei Li, Peiwen Yuan, Yueqi Zhang, Chuyi Tan, Boyuan Pan, Yao Hu, Kan Li
First submitted to arxiv on: 24 Aug 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 Difficulty-Adaptive Self-Consistency (DSC) method leverages difficulty information to adaptively allocate inference resources, reducing the cost of self-consistent decoding for chain-of-thought reasoning tasks. This approach builds upon existing variants like Adaptive self-consistency (ASC) and Early-stopping self-consistency (ESC), which dynamically adjust sampling based on posterior distributions. By incorporating prior knowledge about question difficulty, DSC aims to minimize unnecessary repeated sampling for easy questions, leading to significant cost savings. The method is evaluated on three popular reasoning task categories (arithmetic, commonsense, and symbolic) across six benchmarks, demonstrating superior performance-cost trade-offs compared to strong baselines like ASC and ESC. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The researchers developed a new way to make AI models more efficient. They looked at how people think about math problems and found that they often repeat steps unnecessarily. The new method, called Difficulty-Adaptive Self-Consistency (DSC), uses information about the difficulty of each question to decide when to stop trying to solve it. This makes the process faster and cheaper without losing accuracy. The team tested DSC on different types of math problems and found that it worked better than existing methods in terms of cost while still being accurate. |
Keywords
» Artificial intelligence » Early stopping » Inference
