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Due to the limited availability of labeled data, researchers have investigated few-shot learning methods to tackle this challenge. However, replicating the performance of fully supervised methods remains difficult in few-shot scenarios. This paper addresses two main issues. In terms of data augmentation, existing methods primarily focus on replacing content in the original text, which can potentially distort the semantics. Furthermore, current approaches often neglect sentence features at multiple scales. To overcome these challenges, we utilize ChatGPT to generate enriched data with distinct semantics for the same entities, thereby reducing noisy data. Simultaneously, we employ dynamic convolution to capture multi-scale semantic information in sentences and enhance feature representation based on PubMedBERT. We evaluated the experiments on four biomedical NER datasets (BC5CDR-Disease, NCBI, BioNLP11EPI, BioNLP13GE), and the results exceeded the current state-of-the-art models in most few-shot scenarios, including mainstream large language models like ChatGPT. The results confirm the effectiveness of the proposed method in data augmentation and model generalization.<\/jats:p>","DOI":"10.1186\/s13040-025-00443-y","type":"journal-article","created":{"date-parts":[[2025,4,5]],"date-time":"2025-04-05T01:30:58Z","timestamp":1743816658000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["Few-shot biomedical NER empowered by LLMs-assisted data augmentation and multi-scale feature extraction"],"prefix":"10.1186","volume":"18","author":[{"given":"Di","family":"Zhao","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wenxuan","family":"Mu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiangxing","family":"Jia","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shuang","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yonghe","family":"Chu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiana","family":"Meng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hongfei","family":"Lin","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,4,4]]},"reference":[{"key":"443_CR1","doi-asserted-by":"crossref","unstructured":"Li J, Fei H, Liu J, Wu S, Zhang M, Teng C, et al. Unified named entity recognition as word-word relation classification. In: Proceedings of the AAAI Conference on Artificial Intelligence. AAAI Press, Online; 2022. p. 10965\u201373.","DOI":"10.1609\/aaai.v36i10.21344"},{"key":"443_CR2","unstructured":"Zhang S, Cheng H, Gao J, Poon H. Optimizing bi-encoder for named entity recognition via contrastive learning. In: The Eleventh International Conference on Learning Representations, ICLR 2023. Kigali: OpenReview.net; 2023."},{"key":"443_CR3","doi-asserted-by":"crossref","unstructured":"Ma J, Ballesteros M, Doss S, Anubhai R, Mallya S, Al-Onaizan Y, et al. Label Semantics for Few Shot Named Entity Recognition. In: Findings of the Association for Computational Linguistics: ACL 2022. Dublin: Association for Computational Linguistics; 2022. p. 1956\u201371.","DOI":"10.18653\/v1\/2022.findings-acl.155"},{"key":"443_CR4","doi-asserted-by":"crossref","unstructured":"Wang R, Yu T, Zhao H, Kim S, Mitra S, Zhang R, et al. 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