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Eng."],"published-print":{"date-parts":[[2026,9]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Large-scale, multi-dimensional mixed datasets are characterized by the pervasive \"long-tail distribution.\" This phenomenon results in data sparsity in subspaces defined by multi-dimensional attribute combinations. This sparsity severely hinders data-driven analysis and insights. Existing data-augmentation methods primarily focus on single dimensions, ignoring the complex, intrinsic multi-dimensional correlations of the real world, which leads to generated samples that lack logic and realism. To address this challenge, we propose a systematic, on-demand, and fine-grained sample augmentation framework. Our core idea is to precisely locate and augment data-sparse regions from a multi-dimensional combinatorial perspective. For textual data, we have designe two flexible augmentation modes. The first, \"augmentation by subset scope\", adopts a strategy of model merging and incremental updates. The second, \"augmentation by topic\", proposes a heuristic search algorithm based on the \"Explore-Exploit\" paradigm. For numerical data, we pre-construct a global distribution index to achieve efficient identification of sparse intervals. In the sample generation phase, we combine Large Language Models, Retrieval-Augmented Generation, and Chain-of-Thought techniques to ensure that the generated samples meet high-fidelity standards in semantic logic and contextual style. Extensive experiments on real-world datasets demonstrate that our method outperforms baseline approaches in query response speed and the efficiency of sparse region discovery, while maintaining topic coherence and accuracy.<\/jats:p>","DOI":"10.1007\/s41019-025-00334-6","type":"journal-article","created":{"date-parts":[[2026,4,9]],"date-time":"2026-04-09T01:25:23Z","timestamp":1775697923000},"page":"687-705","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["On-Demand Augmentation for Long-Tail Data Through Subset- and Topic-Driven Sparsity Identification Framework"],"prefix":"10.1007","volume":"11","author":[{"given":"Kun","family":"Wu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yaxi","family":"Hou","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shan","family":"Yang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Linlin","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nan","family":"Lu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaosu","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hanqi","family":"Zheng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fei","family":"Ye","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhenying","family":"He","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,4,9]]},"reference":[{"key":"334_CR1","unstructured":"Kandpal N, Deng H, Roberts A, Wallace E, Raffel C (2023) Large language models struggle to learn long-tail knowledge. 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