Understanding Before Reasoning: Enhancing Chain-of-Thought with Iterative Summarization Pre-Prompting (2501.04341v1)

Abstract: Chain-of-Thought (CoT) Prompting is a dominant paradigm in LLMs to enhance complex reasoning. It guides LLMs to present multi-step reasoning, rather than generating the final answer directly. However, CoT encounters difficulties when key information required for reasoning is implicit or missing. This occurs because CoT emphasizes the sequence of reasoning steps while overlooking the early extraction of essential information. We propose a pre-prompting method called Iterative Summarization Pre-Prompting (ISP^2) to refine LLM reasoning when key information is not explicitly provided. First, entities and their corresponding descriptions are extracted to form potential key information pairs. Next, we use a reliability rating to assess these pairs, then merge the two lowest-ranked pairs into a new entity description. This process is repeated until a unique key information pair is obtained. Finally, that pair, along with the original question, is fed into LLMs to produce the answer. Extensive experiments demonstrate a 7.1% improvement compared to existing methods. Unlike traditional prompting, ISP^2 adopts an inductive approach with pre-prompting, offering flexible integration into diverse reasoning frameworks. The code is available at https://github.com/zdhgreat/ISP-2.

• The paper demonstrates that iterative summarization pre-prompting enhances LLM reasoning by ensuring comprehensive context extraction.
• It refines the Chain-of-Thought approach by iteratively identifying and validating low-reliability information pairs, yielding a 7.1% boost on benchmarks.
• The methodology offers practical integration into AI applications, improving automated tutoring, conversational agents, and decision support systems.

Enhancing Chain-of-Thought Prompting with Iterative Summarization Pre-Prompting

The paper "Understanding Before Reasoning: Enhancing Chain-of-Thought with Iterative Summarization Pre-Prompting" presents an advancement in the domain of LLMs by addressing the limitations of traditional Chain-of-Thought (CoT) Prompting. CoT is widely used in guiding LLMs through complex reasoning tasks by emulating human-like problem-solving steps. However, CoT tends to overlook the critical step of extracting implicit or missing information that is essential for thorough reasoning.

The researchers propose a novel pre-prompting strategy — Iterative Summarization Pre-Prompting ($\text{ISP}^{2}$) — aimed at refining LLMs' reasoning capabilities, particularly when crucial information is implicitly embedded or absent in the input. The methodology employs an iterative process of extracting potential key information pairs and assessing their reliability. By focusing on the information pairs with the lowest reliability scores through iterative summarization, the model progressively gathers a robust understanding of the problem context before reasoning.

The paper provides an in-depth evaluation of $\text{ISP}^{2}$ across several reasoning benchmarks including GSM8K, AddSub, SVAMP, AQuA, CommonsenseQA, and StrategyQA. Empirical results underline a 7.1% enhancement over traditional CoT methodologies on average, demonstrating the efficacy of $\text{ISP}^{2}$. Noteworthy improvements were observed consistently across different task types, signifying its robust applicability. This indicates that incorporating a pre-prompting stage fundamentally contributes to improving LLMs' reasoning output by ensuring comprehensive understanding and information synthesis before engaging in reasoning.

Further implications of the research suggest that $\text{ISP}^{2}$ can be seamlessly integrated into various reasoning frameworks and plug-and-play applications, thereby extending its utility beyond specific LLM architectures. The approach can enhance the extraction of accurate and relevant content necessary for producing reliable reasoning and solutions to complex query scenarios. Long-term, this may promote advancements in LLM-driven applications across fields requiring nuanced understanding and precise information processing such as automated tutoring systems, advanced conversational agents, and decision support systems.

In conclusion, this research introduces an effective methodology for overcoming the inherent information extraction limitations seen in conventional reasoning paradigms within LLMs. By infusing a pre-prompting strategy that emphasizes iterative summarization and assessment of key information, the authors provide a tangible step forward in the development of more intelligent and context-aware AI models. Future exploration in this area can dive deeper into optimizing the integration of $\text{ISP}^{2}$ with other reasoning techniques, potentially leading to even greater enhancements in LLM capabilities.