Large Language Models (LLMs), such as GPT-4 and Llama 2, show remarkable proficiency in a wide range of natural language processing (NLP) tasks. Despite their effectiveness, the high costs associated with their use pose a challenge. We present LlamBERT, a hybrid approach that leverages LLMs to annotate a small subset of large, unlabeled databases and uses the results for fine-tuning transformer encoders like BERT and RoBERTa. This strategy is evaluated on two diverse datasets: the IMDb review dataset and the UMLS Meta-Thesaurus. Our results indicate that the LlamBERT approach slightly compromises on accuracy while offering much greater cost-effectiveness.
@article{arxiv.2403.15938,
title = {LlamBERT: Large-scale low-cost data annotation in NLP},
author = {Bálint Csanády and Lajos Muzsai and Péter Vedres and Zoltán Nádasdy and András Lukács},
journal= {arXiv preprint arXiv:2403.15938},
year = {2024}
}