LLM-generated warm starts improve active learning on low- and medium-dimensional software engineering tasks but underperform Gaussian process methods on high-dimensional tasks.
Discovering General-Purpose Active Learning Strategies
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abstract
We propose a general-purpose approach to discovering active learning (AL) strategies from data. These strategies are transferable from one domain to another and can be used in conjunction with many machine learning models. To this end, we formalize the annotation process as a Markov decision process, design universal state and action spaces and introduce a new reward function that precisely model the AL objective of minimizing the annotation cost. We seek to find an optimal (non-myopic) AL strategy using reinforcement learning. We evaluate the learned strategies on multiple unrelated domains and show that they consistently outperform state-of-the-art baselines.
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Can Large Language Models Improve SE Active Learning via Warm-Starts?
LLM-generated warm starts improve active learning on low- and medium-dimensional software engineering tasks but underperform Gaussian process methods on high-dimensional tasks.