FamiCom: Further Demystifying Prompts for Language Models with Task-Agnostic Performance Estimation

• URL: http://arxiv.org/abs/2406.11243v1
• Date: Mon, 17 Jun 2024 06:14:55 GMT
• Title: FamiCom: Further Demystifying Prompts for Language Models with Task-Agnostic Performance Estimation
• Authors: Bangzheng Li, Ben Zhou, Xingyu Fu, Fei Wang, Dan Roth, Muhao Chen,
• Abstract summary: Language models have shown impressive in-context-learning capabilities. We propose a measure called FamiCom, providing a more comprehensive measure for task-agnostic performance estimation.
• Score: 73.454943870226
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Language models have shown impressive in-context-learning capabilities, which allow them to benefit from input prompts and perform better on downstream end tasks. Existing works investigate the mechanisms behind this observation, and propose label-agnostic prompt metrics that can better estimate end-task performances. One popular approach is using perplexity as a way to measure models' familiarity with the prompt. While showing consistent improvements on in-domain tasks, we found that familiarity metrics such as perplexity cannot accurately estimate performance in complicated situations such as task or domain transferring scenarios. In this work, we propose a revised measure called FamiCom, providing a more comprehensive measure for task-agnostic performance estimation. Specifically, FamiCom combines familiarity with \textit{complexity} -- the inherent difficulty of end tasks, which is an important factor missing from current metrics. Experiments show that FamiCom strongly correlates with end-task performances, producing a 0.85 Spearman's correlation, versus 0.43 of familiarity-only ones'. We further apply FamiCom to automatic prompt and demonstration selection, and outperform existing methods and baselines by more than 7.0% in accuracy.

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