零样本适应新领域，LELA让大模型做实体链接又快又准，学术干货值得一读
arXiv:2601.05192v2 Announce Type: replace Abstract: Entity linking (mapping ambiguous mentions in text to entities in a knowledge base) is a foundatio…
跨语言生物医学实体链接新方案，利用别名检索增强召回，LLM精排提升准确率，值得NLP和生物信息领域关注。
arXiv:2605.27380v1 Announce Type: cross Abstract: Cross-lingual biomedical entity linking (BEL) maps mentions in any language to unique identifiers in…
LLM驱动的实体链接新框架，零样本迁移到新领域无需额外训练数据。
arXiv:2605.26956v1 Announce Type: new Abstract: Entity linking is a key component of many downstream NLP systems, yet existing approaches are often ti…
生物医学实体链接新突破：利用生成式重排序提升实体匹配精度，已被SIGIR 2026接收。
arXiv:2605.22501v1 Announce Type: new Abstract: Despite recent progress, Biomedical Entity Linking (BEL) with large language models (LLMs) remains com…
基于最新研究的实体链接代理，为问答系统提供精准知识检索与链接能力
arXiv:2508.03865v4 Announce Type: replace Abstract: Some Question Answering (QA) systems rely on knowledge bases (KBs) to provide accurate answers. En…