IDEAL: Influence-Driven Selective Annotations Empower In-Context Learners in Large Language Models
ICLR 2024(2023)
摘要
In-context learning is a promising paradigm that utilizes in-context examples
as prompts for the predictions of large language models. These prompts are
crucial for achieving strong performance. However, since the prompts need to be
sampled from a large volume of annotated examples, finding the right prompt may
result in high annotation costs. To address this challenge, this paper
introduces an influence-driven selective annotation method that aims to
minimize annotation costs while improving the quality of in-context examples.
The essence of our method is to select a pivotal subset from a large-scale
unlabeled data pool to annotate for the subsequent sampling of prompts.
Specifically, a directed graph is first constructed to represent unlabeled
data. Afterward, the influence of candidate unlabeled subsets is quantified
with a diffusion process. A simple yet effective greedy algorithm for unlabeled
data selection is lastly introduced. It iteratively selects the data if it
provides a maximum marginal gain with respect to quantified influence. Compared
with previous efforts on selective annotations, our influence-driven method
works in an end-to-end manner, avoids an intractable explicit balance between
data diversity and representativeness, and enjoys theoretical support.
Experiments confirm the superiority of the proposed method on various
benchmarks, achieving better performance under lower time consumption during
subset selection. The project page is available at
https://skzhang1.github.io/IDEAL/.
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关键词
in-context learning,large language models,active learning
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