Iterative Resolution of Prompt Ambiguities Using a Progressive Cutting-Search Approach
This addresses the issue of imprecise instructions in AI interactions for users, but it is incremental as it builds on existing iterative refinement techniques.
The paper tackles the problem of natural language ambiguity in generative AI systems by proposing an iterative approach that uses clarification questions and alternative proposals to resolve uncertainties, resulting in superior accuracy, competitive resolution times, and higher user satisfaction compared to one-shot methods.
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.