Summary of Strategic Chain-of-thought: Guiding Accurate Reasoning in Llms Through Strategy Elicitation, by Yu Wang et al.
Strategic Chain-of-Thought: Guiding Accurate Reasoning in LLMs through Strategy Elicitation
by Yu Wang, Shiwan Zhao, Zhihu Wang, Heyuan Huang, Ming Fan, Yubo Zhang, Zhixing Wang, Haijun Wang, Ting Liu
First submitted to arxiv on: 5 Sep 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL); Human-Computer Interaction (cs.HC)
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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 Strategic Chain-of-Thought (SCoT) paradigm is a novel methodology designed to refine large language model (LLM) performance by integrating strategic knowledge prior to generating intermediate reasoning steps. This approach addresses the challenge of CoT methods’ instability, ensuring high-quality generated reasoning paths and improving overall LLM performance. SCoT employs a two-stage approach within a single prompt, first eliciting an effective problem-solving strategy that guides the generation of high-quality CoT paths and final answers. Our experiments across eight challenging reasoning datasets demonstrate significant improvements using the Llama3-8b model, including a 21.05% increase on the GSM8K dataset and 24.13% on the Tracking_Objects dataset. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The Chain-of-Thought (CoT) method is trying to make big language models better at solving problems by giving them some extra knowledge first. This helps the models do their job more accurately, but it’s been hard to get this process right. To fix this, scientists created a new way called SCoT that works like this: they ask the model for a good plan to solve a problem, then use that plan to make sure the rest of the thinking is correct and makes sense. They tested this method on lots of tricky problems and found it did much better than before! This could be really important for things like artificial intelligence and making computers smarter. |
Keywords
» Artificial intelligence » Large language model » Prompt
