Quantifying Language Models' Sensitivity to Spurious Features in Prompt Design.¶
Sclar, M., Choi, Y., Tsvetkov, Y., & Suhr, A. (2024). Quantifying Language Models' Sensitivity to Spurious Features in Prompt Design. Proceedings of ICLR 2024.
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- Elicitation Channel Contribution
- Language-model prompting: the same model on the same query produces different outputs under different prompts; prompt engineering is elicitation-channel design, and modern evaluation randomises across templates and reports sensitivity bands.
This sourceDemonstrates that trivial prompt-formatting changes produce large output variation, motivating randomization across templates and reporting of sensitivity bands.
- Language-model prompting: the same model on the same query produces different outputs under different prompts; prompt engineering is elicitation-channel design, and modern evaluation randomises across templates and reports sensitivity bands.
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