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@ShahidNShah
@ShahidNShah
Increasing Large Language Model Accuracy for Care-Seeking Advice Using Prompts Reflecting Human Reasoning Strategies in the Real World: Validation Study
Current prompting techniques for large language models (LLMs), such as ChatGPT, mainly focus on well-structured, low-uncertainty problems; yet, many real-world tasks (eg, care-seeking decisions) are ill-defined and involve high uncertainty. Naturalistic decision-making (NDM) specifically analyzes how humans make accurate decisions in such settings, but NDM concepts have not yet been applied to LLM prompt engineering.
Medigy Insights
Prompting AI models to follow human decision-making strategies significantly improved the accuracy of care-seeking recommendations, particularly in identifying situations where self-care is appropriate. The findings show that how AI is instructed to reason can be just as important as the model itself when handling complex health decisions.
Continue reading at biomedeng.jmir.org
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