In-context Example Selection with Influences

02/21/2023

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by   Tai Nguyen, et al.

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In-context learning (ICL) is a powerful paradigm emerged from large language models (LLMs). Despite its promises, ICL performance is known to be highly sensitive to input examples. In this work, we use in-context influences to analyze few-shot ICL performance directly from the in-context examples. Our proposed influence-based example selection method outperforms most baselines when evaluated on 10 SuperGlue tasks and stably scales with increasing k-shot. The analysis finds up to a 22.2 and negatively influential examples. In a case study, we apply our influence-based framework to quantify the phenomena of recency bias in example ordering for few-shot ICL.