This work presents our participation in the EvalLLM 2025 challenge on biomedical Named Entity Recognition (NER) and health event extraction in French (few-shot setting). For NER, we propose three approaches combining large language models (LLMs), annotation guidelines, synthetic data, and post-processing: (1) in-context learning (ICL) with GPT-4.1, incorporating automatic selection of 10 examples and a summary of the annotation guidelines into the prompt, (2) the universal NER system GLiNER, fine-tuned on a synthetic corpus and then verified by an LLM in post-processing, and (3) the open LLM LLaMA-3.1-8B-Instruct, fine-tuned on the same synthetic corpus. Event extraction uses the same ICL strategy with GPT-4.1, reusing the guideline summary in the prompt. Results show GPT-4.1 leads with a macro-F1 of 61.53% for NER and 15.02% for event extraction, highlighting the importance of well-crafted prompting to maximize performance in very low-resource scenarios.

翻译：本研究介绍了我们参与EvalLLM 2025挑战赛在法语生物医学命名实体识别（NER）与健康事件抽取（少样本设定）方面的工作。针对NER任务，我们提出了三种结合大语言模型（LLM）、标注指南、合成数据及后处理的方法：（1）基于GPT-4.1的上下文学习（ICL），在提示中自动选取10个示例并整合标注指南摘要；（2）通用NER系统GLiNER，在合成语料上进行微调后通过LLM进行后处理验证；（3）开源LLM LLaMA-3.1-8B-Instruct，在同一合成语料上进行微调。事件抽取任务采用相同的GPT-4.1 ICL策略，在提示中复用指南摘要。实验结果表明，GPT-4.1在NER任务上以61.53%的宏F1值领先，在事件抽取任务上达到15.02%，凸显了在极低资源场景中精心设计提示对提升性能的关键作用。