ActiveLLM: Large Language Model-based Active Learning for Textual Few-Shot Scenarios
<|reference_start|>ActiveLLM: Large Language Model-based Active Learning for Textual Few-Shot Scenarios: Active learning is designed to minimize annotation efforts by prioritizing instances that most enhance learning. However, many active learning strategies struggle with a 'cold start' problem, needing substantial initial data to be effective. This limitation often reduces their utility for pre-trained models, which already perform well in few-shot scenarios. To address this, we introduce ActiveLLM, a novel active learning approach that leverages large language models such as GPT-4, Llama 3, and Mistral Large for selecting instances. We demonstrate that ActiveLLM significantly enhances the classification performance of BERT classifiers in few-shot scenarios, outperforming both traditional active learning methods and the few-shot learning method SetFit. Additionally, ActiveLLM can be extended to non-few-shot scenarios, allowing for iterative selections. In this way, ActiveLLM can even help other active learning strategies to overcome their cold start problem. Our results suggest that ActiveLLM offers a promising solution for improving model performance across various learning setups.<|reference_end|>
@article{bayer2024activellm:,
    title={ActiveLLM: Large Language Model-based Active Learning for Textual
  Few-Shot Scenarios},
    author={Markus Bayer and Christian Reuter},
    journal={arXiv preprint arXiv:2405.10808},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.10808},
    primaryClass={cs.CL}
}