Strategic Chain-of-Thought: Guiding Accurate Reasoning in LLMs through Strategy Elicitation (AI summary)

Key Points

1. The Chain-of-Thought (CoT) paradigm has emerged as a critical approach for enhancing the reasoning capabilities of large language models (LLMs), but it often exhibits instability due to the inability to consistently ensure the quality of generated reasoning paths.

2. To address this challenge, the paper proposes the Strategic Chain-of-Thought (SCoT), a novel methodology designed to refine LLM performance by integrating strategic knowledge prior to generating intermediate reasoning steps.

3. SCoT employs a two-stage approach within a single prompt: first eliciting an effective problem-solving strategy, which is then used to guide the generation of high-quality CoT paths and final answers.

4. Experiments across eight challenging reasoning datasets demonstrate significant improvements using the SCoT method, including a 21.05% increase on the GSM8K dataset and 24.13% on the Tracking Objects dataset, compared to standard CoT.

5. The paper further extends the SCoT framework to develop a few-shot method with automatically matched demonstrations, yielding even stronger results.

6. The findings underscore the efficacy of SCoT, highlighting its potential to substantially enhance LLM performance in complex reasoning tasks.

Summary

Introduction of Strategic Chain-of-Thought (SCoT) Methodology
This research paper introduces a novel methodology called Strategic Chain-of-Thought (SCoT) to enhance the reasoning capabilities of large language models (LLMs). The Chain-of-Thought (CoT) paradigm, a widely adopted approach for improving LLM performance in reasoning tasks, often exhibits instability due to the inconsistent quality of the generated reasoning paths. To address this challenge, the authors propose the SCoT approach, which integrates strategic knowledge to guide the generation of high-quality CoT paths and final answers.

Two-Stage Process of SCoT Method
The SCoT method employs a two-stage process within a single prompt. First, it elicits an effective problem-solving strategy from the LLM, which is then used to direct the generation of the CoT paths and the final answer. This strategic knowledge helps ensure the quality and consistency of the reasoning process, mitigating the variability inherent in the standard CoT approach. The researchers conducted experiments across eight challenging reasoning datasets spanning various domains, including mathematical reasoning, commonsense reasoning, physical reasoning, spatial reasoning, and multi-hop reasoning. The results demonstrate significant improvements in performance when using the SCoT approach. For example, the Llama3-8b model showed a 21.05% increase in accuracy on the GSM8K dataset and a 24.13% improvement on the Tracking Objects dataset, compared to the standard CoT method. Furthermore, the authors extend the SCoT framework to develop a few-shot method that automatically matches relevant demonstrations based on the generated strategic knowledge. This few-shot SCoT approach yields even stronger results, highlighting the effectiveness of integrating strategic knowledge to enhance LLM reasoning capabilities.

Key Contributions and Findings
The key contributions of this work are the introduction of the SCoT methodology, which leverages strategic knowledge to guide accurate reasoning in LLMs, and the demonstration of its substantial performance improvements across a diverse range of reasoning tasks. These findings underline the potential of the SCoT approach to significantly enhance the complex reasoning abilities of large language models, making it a promising and impactful advancement in the field of artificial intelligence."

Reference: https://arxiv.org/abs/2409.032...