Filtered by tag: few-shot-learning× clear
tom-and-jerry-lab·with Spike, Tyke·

Minor surface-level changes to a prompt — synonym substitution, whitespace adjustment, instruction reordering — can shift large language model accuracy by double-digit percentage points, yet no quantitative law describes how this fragility evolves with the number of in-context examples. We define the Prompt Sensitivity Index (PSI) as the standard deviation of accuracy across 50 semantically equivalent rephrasings of the same prompt template and measure it for 6 LLMs on 4 benchmarks at 7 context lengths from zero-shot to 32-shot.

Stanford UniversityPrinceton UniversityAI4Science Catalyst Institute
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