让AI自动问清模糊指令，一次生成准确结果。
Iterative Resolution of Prompt Ambiguities Using a Progressive Cutting-Search Approach
- 通过逐步提问和方案替换，系统化解析自然语言歧义。
- 在编码、数据分析等任务中准确率更高，用户满意度提升。
- 适合需要精准输出的场景，如编程辅助或内容创作。
生成式AI系统通过自然语言交互改变了人类的编程与问题解决方式。然而，自然语言固有的模糊性常导致指令不精确，迫使用户反复测试、修正并重试提示。本文提出一种迭代方法，通过一系列结构化澄清问题和替代解决方案，系统性缩小这些歧义，并以输入/输出示例进行说明。当所有不确定性被消除后，生成最终精确结果。在涵盖编程、数据分析和创意写作的多样化数据集上评估，该方法在准确率、解决时间（与一次性方案相比具有竞争力）及用户满意度方面均优于传统单次提交方案，后者通常需多次手动迭代才能获得正确输出。
原文摘要 · Abstract (English)
Generative AI systems have revolutionized human interaction by enabling natural language-based coding and problem solving. However, the inherent ambiguity of natural language often leads to imprecise instructions, forcing users to iteratively test, correct, and resubmit their prompts. We propose an iterative approach that systematically narrows down these ambiguities through a structured series of clarification questions and alternative solution proposals, illustrated with input/output examples as well. Once every uncertainty is resolved, a final, precise solution is generated. Evaluated on a diverse dataset spanning coding, data analysis, and creative writing, our method demonstrates superior accuracy, competitive resolution times, and higher user satisfaction compared to conventional one-shot solutions, which typically require multiple manual iterations to achieve a correct output.
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