Evaluating the Robustness and Fairness of Instruction-Tuned LLMs in Clinical Tasks: Implications for Performance Variability and Demographic Fairness

Practical Solutions and Value of Instruction-Tuned LLMs in Clinical Tasks

Addressing Sensitivity to Instruction Phrasing

LLMs have been enhanced to handle various tasks with natural language instructions, but their performance is sensitive to how instructions are phrased. This creates challenges, especially in specialized domains like medicine, where model performance can have significant consequences for patient care.

Robustness and Fairness in Clinical LLMs

Research highlights substantial performance variability and fairness issues across general and specialized LLMs when handling clinical tasks. The study emphasizes the need for further research in this area and the release of code and prompts to support ongoing investigations.

Improving LLM Performance

Techniques like Reinforcement Learning from Human Feedback and fine-tuning with labeled data have enhanced LLMs to solve tasks with minimal examples or instructions. However, prompt construction sensitivity affects performance in few-shot and zero-shot settings.

Evaluating LLM Robustness and Fairness

The study evaluates the robustness of LLMs to natural variations in instructional phrasings for clinical tasks, highlighting significant performance variability and fairness implications. It emphasizes the need for improved LLM robustness in critical clinical tasks.

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