Exploring Zero-Shot Cross-Lingual Biomedical Concept Normalization via Large Language Models

Over the past few years, discriminative and generative large language models (LLMs) have emerged as the predominant approaches in natural language processing. However, despite significant advancements, there remains a gap in comparing the performance of discriminative and generative LLMs in cross-lingual biomedical concept normalization. In this paper, we perform a comparative study across several LLMs on the challenging task of cross-lingual biomedical concept normalization via dense retrieval. We utilize the XL-BEL dataset covering 10 languages to evaluate the model's capacity to generalize across various linguistic contexts without further adaptation. The experimental findings demonstrate that e5, a discriminative model, exhibited superior performance, whereas BioMistral emerged as the top-performing generative LLM. The code for reproducing the experiments is available at: https://github.com/hrouhizadeh/zsh_cl_bcn.