From Selection to Generation: A Survey of LLM-based Active Learning

TL;DR

This survey reviews how Large Language Models are transforming active learning by enhancing data selection, generation, and annotation, highlighting recent advances, applications, challenges, and future directions.

Contribution

It provides a comprehensive taxonomy and analysis of LLM-based active learning techniques, emphasizing their roles in data generation and selection, and discusses their impact across domains.

Findings

LLMs enable more effective data selection in AL.

LLMs can generate new training data for AL.

The survey identifies open challenges and future research directions.

Abstract

Active Learning (AL) has been a powerful paradigm for improving model efficiency and performance by selecting the most informative data points for labeling and training. In recent active learning frameworks, Large Language Models (LLMs) have been employed not only for selection but also for generating entirely new data instances and providing more cost-effective annotations. Motivated by the increasing importance of high-quality data and efficient model training in the era of LLMs, we present a comprehensive survey on LLM-based Active Learning. We introduce an intuitive taxonomy that categorizes these techniques and discuss the transformative roles LLMs can play in the active learning loop. We further examine the impact of AL on LLM learning paradigms and its applications across various domains. Finally, we identify open challenges and propose future research directions. This survey aims to serve as an up-to-date resource for researchers and practitioners seeking to gain an intuitive understanding of LLM-based AL techniques and deploy them to new applications.