专精食品实体链接的开源大模型，准确率最高达98%。
FoodSEM: Large Language Model Specialized in Food Named-Entity Linking
- 基于指令-响应格式微调，精准链接食品文本到多个本体
- 在多个数据集上F1最高达98%，显著优于通用模型
- 适合食品领域研究者和需要高精度实体链接的项目
本文提出FoodSEM，一个针对食品相关本体进行命名实体链接（NEL）的先进开源大语言模型。据我们所知，现有通用大模型或特定领域模型无法准确完成食品领域的实体链接任务。通过指令-响应（IR）范式，FoodSEM将文本中提及的食品实体映射至FoodOn、SNOMED-CT及Hansard分类体系等多个本体。与同类模型相比，FoodSEM表现达到顶尖水平，部分数据集上F1分数高达98%。与零样本、单样本及少样本提示基线对比分析表明，其性能显著优于未微调版本。本文主要贡献包括：（1）发布可用于大模型微调与评估的食品标注语料库（以IR格式）；（2）发布一个强大的食品领域语义理解模型；（3）为未来食品实体链接任务提供强基准。
原文摘要 · Abstract (English)
This paper introduces FoodSEM, a state-of-the-art fine-tuned open-source large language model (LLM) for named-entity linking (NEL) to food-related ontologies. To the best of our knowledge, food NEL is a task that cannot be accurately solved by state-of-the-art general-purpose (large) language models or custom domain-specific models/systems. Through an instruction-response (IR) scenario, FoodSEM links food-related entities mentioned in a text to several ontologies, including FoodOn, SNOMED-CT, and the Hansard taxonomy. The FoodSEM model achieves state-of-the-art performance compared to related models/systems, with F1 scores even reaching 98% on some ontologies and datasets. The presented comparative analyses against zero-shot, one-shot, and few-shot LLM prompting baselines further highlight FoodSEM's superior performance over its non-fine-tuned version. By making FoodSEM and its related resources publicly available, the main contributions of this article include (1) publishing a food-annotated corpora into an IR format suitable for LLM fine-tuning/evaluation, (2) publishing a robust model to advance the semantic understanding of text in the food domain, and (3) providing a strong baseline on food NEL for future benchmarking.
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