ActiveLLM: Large Language Model-based Active Learning for Textual Few-Shot Scenarios
| Autor: | Bayer, Markus, Reuter, Christian |
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| Rok vydání: | 2024 |
| Předmět: | |
| Druh dokumentu: | Working Paper |
| Popis: | 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. Comment: 18 pages, 7 figures, 4 tables |
| Databáze: | arXiv |
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