小型大模型在关系抽取任务上可媲美前沿模型

报道来源 [3]

1. arXiv cs.AI TIER_1 English(EN) · Dragos-Mitrut Vasile, Elena-Simona Apostol, Stefan-Adrian Toma, Adrian Paschke, Ciprian-Octavian Truica · 2026-07-01 04:00

   大型语言模型跨语言关系抽取：罗马尼亚语的零样本、少样本和微调评估

   arXiv:2606.31718v1 Announce Type: cross Abstract: Relation extraction (RE) for low-resource languages is typically constrained by the lack of annotated corpora. We investigate the feasibility of cross-lingual RE for Romanian by combining automatic dataset translation with large l…

2. arXiv cs.AI TIER_1 English(EN) · Ciprian-Octavian Truica · 2026-06-30 14:22

   大型语言模型跨语言关系抽取：罗马尼亚语的零样本、少样本和微调评估

   Relation extraction (RE) for low-resource languages is typically constrained by the lack of annotated corpora. We investigate the feasibility of cross-lingual RE for Romanian by combining automatic dataset translation with large language model (LLM) inference. We translate the Se…

3. Hugging Face Daily Papers TIER_1 English(EN) · 2026-06-21 17:24

   十亿美元以下，超级前沿：小型语言模型在通用和文学关系抽取方面可与零样本前沿大语言模型相媲美

   Large language models (LLMs) achieve strong relation extraction (RE), but their computational demands and reliance on proprietary APIs limit deployment in resource-constrained or privacy-sensitive settings. We investigate how far small language models (SLMs) can close this gap ac…