Instruction Phrasing

Instruction phrasing is a critical area of research focusing on how the wording of instructions impacts the performance of large language models (LLMs). Current research investigates methods for generating large, diverse instruction datasets, often employing techniques like evolutionary algorithms or leveraging LLMs themselves to create synthetic instructions. This work is crucial because even small variations in instruction phrasing significantly affect LLM accuracy and fairness, particularly in high-stakes applications like healthcare and robotics, highlighting the need for more robust and less brittle models. Improving instruction robustness is therefore essential for broadening the reliable application of LLMs across various domains.