An Examination on the Effectiveness of Divide-and-Conquer Prompting in Large Language Models
Keywords: Program-guided Prompt, Divide-and-Conquer, Foundation Model, Misinformation Detection
Abstract: Foundation models, such as Large language Models (LLMs), have attracted significant amount of interest due to their large number of applications. However, when handling tasks involving repetitive sub-tasks and/or deceptive contents, such as arithmetic calculation and article-level fake news detection, simple instructional prompts suffer from inaccurate responses. Existing works show that more complicated prompting strategies, such as Chain-of-Thoughts and Least-to-Most, can unlock LLM's powerful capacity in diverse areas. Recent researches reveal that simple divide-and-conquer prompting strategy, i.e. simply dividing the input sequence to multiple sub-inputs, can substantially improve LLM's performance in some specific tasks such as misinformation detection. In this paper, we aim at understanding the utility of divide-and-conquer prompting strategy, i.e. on which kind of tasks this strategy gets advantages. Specifically, we provide a theoretic analysis to divide-and-conquer prompting strategy and help us identify the specific tasks where DaC prompting can bring performance boost with theoretic guarantee. We then present two cases (\textbf{large integer arithmetic and fact verification}) where experimental results aligns with our theoretic analysis.
Primary Area: foundation or frontier models, including LLMs
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Submission Number: 5205
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