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Sci."],"published-print":{"date-parts":[[2026,7]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Real-world text classification often faces the challenge of long-tailed data distributions, where most categories contain only a few samples. Directly fine-tuning pre-trained language models (PLMs) on such imbalanced data typically causes semantic space collapse for tail classes and severe overfitting to head classes. To address this, we propose the Long-tail Aware Graph Adapter (LAGA), a novel framework that shifts from full-parameter fine-tuning to structured semantic space adaptation. LAGA first aligns text and label semantics using natural language descriptions to build a unified semantic coordinate system. Crucially, it then freezes the PLM backbone and employs a heterogeneous graph adapter encompassing texts, labels, and learnable prototypes. Through graph neural network propagation, this adapter contextually refines the frozen semantic space. Coupled with dynamic prototype evolution and a tail-aware optimization objective, LAGA forms robust decision boundaries for scarce categories. Extensive experiments on six benchmarks demonstrate that LAGA consistently enhances various PLMs in few-shot, long-tail settings. It achieves superior tail-class recognition and a better performance-efficiency trade-off than strong baselines, including massive large language models (LLMs), proving that graph-based structural adaptation is a highly effective solution for imbalanced text classification.<\/jats:p>","DOI":"10.1007\/s44443-026-00656-z","type":"journal-article","created":{"date-parts":[[2026,3,20]],"date-time":"2026-03-20T11:24:44Z","timestamp":1774005884000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["LAGA: A graph adapter for long-tail text classification via semantic space refinement"],"prefix":"10.1007","volume":"38","author":[{"given":"Shiyu","family":"Wang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gang","family":"Zhou","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mingjing","family":"Lan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jicang","family":"Lu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhufeng","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yi","family":"Xia","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,3,20]]},"reference":[{"key":"656_CR1","doi-asserted-by":"publisher","unstructured":"Cai Y, Wang L, Wang Y, De Melo G, Zhang Y, Wang Y, He L (2024) Medbench: A large-scale chinese benchmark for evaluating medical large language models. 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