用大模型识别西语临床文本中的有害成瘾行为，效果优于英语外语言普遍水平。
FMI@SU ToxHabits: Evaluating LLMs Performance on Toxic Habit Extraction in Spanish Clinical Texts
- 采用少样本提示法结合GPT-4.1，提升西语临床文本实体识别能力。
- 在测试集上取得0.65的F1分数，验证了非英语语种应用潜力。
- 方法适用于医疗文本中成瘾行为的自动化提取，适合临床研究者参考。
本文针对西班牙语临床文本中的有毒习惯命名实体识别问题，提出了一种基于大模型的方法。该方法参与了ToxHabits共享任务的子任务1，旨在检测临床病例报告中物质使用与滥用提及，并将其分类为四类：烟草、酒精、大麻和药物。我们探索了零样本、少样本及提示优化等多种大模型使用策略，实验表明GPT-4.1的少样本提示表现最佳。最终方法在测试集上取得了0.65的F1分数，展示了在非英语语言中实现命名实体识别的可行性与前景。
原文摘要 · Abstract (English)
The paper presents an approach for the recognition of toxic habits named entities in Spanish clinical texts. The approach was developed for the ToxHabits Shared Task. Our team participated in subtask 1, which aims to detect substance use and abuse mentions in clinical case reports and classify them in four categories (Tobacco, Alcohol, Cannabis, and Drug). We explored various methods of utilizing LLMs for the task, including zero-shot, few-shot, and prompt optimization, and found that GPT-4.1's few-shot prompting performed the best in our experiments. Our method achieved an F1 score of 0.65 on the test set, demonstrating a promising result for recognizing named entities in languages other than English.
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