Abstract
Large language models (LLMs) have become the preferred solution for many natural language processing tasks. In low-resource environments such as specialized domains, their few-shot capabilities are expected to deliver high performance. Named Entity Recognition (NER) is a critical task in information extraction that is not covered in recent LLM benchmarks. There is a need for better understanding the performance of LLMs for NER in a variety of settings including languages other than English. This study aims to evaluate generative LLMs, employed through prompt engineering, for few-shot clinical NER. We compare 13 auto-regressive models using prompting and 16 masked models using fine-tuning on 14 NER datasets covering English, French and Spanish. While prompt-based auto-regressive models achieve competitive F1 for general NER, they are outperformed within the clinical domain by lighter biLSTM-CRF taggers based on masked models. Additionally, masked models exhibit lower environmental impact compared to auto-regressive models. Findings are consistent across the three languages studied, which suggests that LLM prompting is not yet suited for NER production in the clinical domain.- Anthology ID:
- 2024.findings-emnlp.400
- Volume:
- Findings of the Association for Computational Linguistics: EMNLP 2024
- Month:
- November
- Year:
- 2024
- Address:
- Miami, Florida, USA
- Editors:
- Yaser Al-Onaizan, Mohit Bansal, Yun-Nung Chen
- Venue:
- Findings
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 6829–6852
- Language:
- URL:
- https://preview.aclanthology.org/remove-affiliations/2024.findings-emnlp.400/
- DOI:
- 10.18653/v1/2024.findings-emnlp.400
- Cite (ACL):
- Marco Naguib, Xavier Tannier, and Aurélie Névéol. 2024. Few-shot clinical entity recognition in English, French and Spanish: masked language models outperform generative model prompting. In Findings of the Association for Computational Linguistics: EMNLP 2024, pages 6829–6852, Miami, Florida, USA. Association for Computational Linguistics.
- Cite (Informal):
- Few-shot clinical entity recognition in English, French and Spanish: masked language models outperform generative model prompting (Naguib et al., Findings 2024)
- PDF:
- https://preview.aclanthology.org/remove-affiliations/2024.findings-emnlp.400.pdf