Papers › LlamBERT: Large-scale low-cost data annotation in NLP

LlamBERT: Large-scale low-cost data annotation in NLP

23 Mar 2024arXiv:2403.15938archive 2025-07-28

Bálint Csanády, Lajos Muzsai, Péter Vedres, Zoltán Nádasdy, András Lukács

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.

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