Strategic Chain-of-Thought: Guiding Accurate Reasoning in LLMs through Strategy Elicitation (2409.03271v1)

Abstract: The Chain-of-Thought (CoT) paradigm has emerged as a critical approach for enhancing the reasoning capabilities of LLMs. However, despite their widespread adoption and success, CoT methods often exhibit instability due to their inability to consistently ensure the quality of generated reasoning paths, leading to sub-optimal reasoning performance. To address this challenge, we propose the \textbf{Strategic Chain-of-Thought} (SCoT), a novel methodology designed to refine LLM performance by integrating strategic knowledge prior to generating intermediate reasoning steps. 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. Our experiments across eight challenging reasoning datasets demonstrate significant improvements, including a 21.05\% increase on the GSM8K dataset and 24.13\% on the Tracking_Objects dataset, respectively, using the Llama3-8b model. Additionally, we extend the SCoT framework to develop a few-shot method with automatically matched demonstrations, yielding even stronger results. These findings underscore the efficacy of SCoT, highlighting its potential to substantially enhance LLM performance in complex reasoning tasks.

Strategic Chain-of-Thought: A Novel Method for Enhancing Reasoning in LLMs

The paper "Strategic Chain-of-Thought: Guiding Accurate Reasoning in LLMs through Strategy Elicitation" introduces an innovative approach for refining the reasoning capabilities of LLMs. The proposed method, termed Strategic Chain-of-Thought (SCoT), aims to address the instability and variability in the quality of reasoning paths generated by traditional Chain-of-Thought (CoT) methods. The researchers present SCoT as a solution that integrates strategic knowledge prior to generating reasoning steps, leading to significant improvements in performance across various reasoning tasks.

Methodology

SCoT employs a two-stage process within a single prompt. The first stage involves eliciting an effective problem-solving strategy, which is then used as guiding strategic knowledge in the second stage, where the final answer is generated. This approach contrasts with existing methods that often rely on voting-based techniques or retrieval-augmented generation frameworks, which are resource-intensive and may require multiple queries or external knowledge sources.

Empirical Results

The authors evaluate SCoT's efficacy on eight challenging datasets, including GSM8K and Tracking Objects. Notably, SCoT achieves a 21.05% improvement in accuracy on the GSM8K dataset and a 24.13% gain on the Tracking Objects dataset using the Llama3-8b model. These results demonstrate SCoT's capacity to produce high-quality reasoning paths and accurate answers more consistently than conventional CoT methodologies.

Comparative Analysis

SCoT differentiates itself from other methods by reducing the reliance on additional computational resources. Voting-based methods like Self-Consistency may demand a multitude of generated reasoning paths, and RAG-based methods require external information sources, leading to increased complexity and resource consumption. SCoT simplifies the process by using a strategic elicitation-and-application model without necessitating external data, thereby decreasing computational overhead.

Implications and Future Work

SCoT's integration of strategic knowledge opens new avenues for enhancing the reliability and accuracy of reasoning tasks in LLMs. Its potential applications extend beyond natural language processing to more complex cognitive tasks that require nuanced strategization. Future research could explore the automatic generation of strategic knowledge templates, investigate the scalability of SCoT with larger LLMs, and extend its applicability to a broader range of reasoning scenarios.

Overall, the Strategic Chain-of-Thought method represents a meaningful contribution to advancing the state-of-the-art in reasoning by LLMs. While not employing multi-query strategies or external knowledge dependence, SCoT refines reasoning pathways through strategic knowledge elicitation, demonstrating efficacy across diverse domains. This paper illustrates a significant step toward more efficient, accurate, and reliable reasoning in AI models.