Coverage-based Example Selection for In-Context Learning

TL;DR

This paper introduces a coverage-based method for selecting in-context examples that improves the informativeness and diversity of examples, leading to better performance across multiple tasks and models.

Contribution

It proposes BERTScore-Recall (BSR) and set-level metrics for improved example selection in in-context learning, outperforming traditional methods.

Findings

BSR outperforms standard similarity metrics in example selection.

Set-BSR improves coverage and performance on compositional tasks.

The method surpasses training-based approaches without additional training.

Abstract

In-context learning (ICL), the ability of large language models to perform novel tasks by conditioning on a prompt with a few task examples, requires these examples to be informative about the test instance. The standard approach of independently ranking and selecting the most similar examples selects redundant examples while omitting important information. In this work, we show that BERTScore-Recall (BSR) selects better examples that demonstrate more of the salient aspects, e.g. reasoning patterns, of the test input. We further extend BSR and many standard metrics to easily optimizable set-level metrics, giving still better coverage of those salient aspects. On 15 datasets spanning 6 tasks and with 7 diverse LLMs, we show that (1) BSR is the superior metric for in-context example selection across the board, and (2) for compositional tasks, set selection using Set-BSR outperforms independent ranking by up to 17 points on average and, despite being training-free, surpasses methods that leverage task or LLM-specific training.