Hypothesis- and Structure-based prompting for medical and business diagnosis

Juyeon Heo, Kyunghyun Lee, Hyonkeun Joh, Umang Bhatt, Adrian Weller

Primary Area: generative models

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Keywords: Large Language Models, Prompting method, Medical diagnosis, Business consulting application

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Abstract:

In real-world scenarios like healthcare and business, tackling many-to-one problems is challenging but crucial. Take medical diagnosis: A patient's chief complaint can be caused by various diseases, yet time and resource constraints make identifying the cause via difficult.
To tackle these issues, our study introduces Hypothesis-based and Structure-based (HS) prompting, a method designed to enhance the problem-solving capabilities of Large Language Models (LLMs). Our approach starts by efficiently breaking down the problem space using a Mutually Exclusive and Collectively Exhaustive (MECE) framework. Armed with this structure, LLMs generate, prioritize, and validate hypotheses through targeted questioning and data collection. The ability to ask the right questions is crucial for pinpointing the root cause of a problem accurately. We provide an easy-to-follow guide for crafting examples, enabling users to develop tailored HS prompts for specific tasks. We validate our method through diverse case studies in business consulting and medical diagnosis, which are further evaluated by domain experts. Interestingly, adding one sentence ``You can request one data in each response if needed'' initiates human interaction and improves performance.

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Submission Number: 983