让大模型零样本识别意大利语命名实体，突破标注数据依赖
SLIMER-IT: Zero-Shot NER on Italian Language
- 用提示词注入定义和规则，指导大模型完成零样本命名实体识别
- 在未见过的实体类型上表现超越现有模型，准确率提升显著
- 专为意大利语设计，适合多语言低资源场景的研究者使用
传统命名实体识别（NER）将任务视为BIO序列标注问题，虽在特定任务中表现优异，但需大量标注数据，难以泛化到分布外输入或未见实体类型。相比之下，大语言模型（LLMs）展现出强大的零样本能力。尽管已有研究聚焦英语零样本NER，其他语言仍鲜有探索。本文构建了零样本NER评估框架，并应用于意大利语。提出SLIMER-IT，即意大利语版SLIMER，一种通过注入定义与指南的提示词进行指令微调的方法，实现零样本NER。与多个先进模型对比显示，该方法在从未见过的实体标签上表现更优。
原文摘要 · Abstract (English)
Traditional approaches to Named Entity Recognition (NER) frame the task into a BIO sequence labeling problem. Although these systems often excel in the downstream task at hand, they require extensive annotated data and struggle to generalize to out-of-distribution input domains and unseen entity types. On the contrary, Large Language Models (LLMs) have demonstrated strong zero-shot capabilities. While several works address Zero-Shot NER in English, little has been done in other languages. In this paper, we define an evaluation framework for Zero-Shot NER, applying it to the Italian language. Furthermore, we introduce SLIMER-IT, the Italian version of SLIMER, an instruction-tuning approach for zero-shot NER leveraging prompts enriched with definition and guidelines. Comparisons with other state-of-the-art models, demonstrate the superiority of SLIMER-IT on never-seen-before entity tags.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。