{"ID":2840505,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2511.13189","arxiv_id":"2511.13189","title":"Large Language Models Meet Extreme Multi-label Classification: Scaling and Multi-modal Framework","abstract":"Foundation models have revolutionized artificial intelligence across numerous domains, yet their transformative potential remains largely untapped in Extreme Multi-label Classification (XMC). Queries in XMC are associated with relevant labels from extremely large label spaces, where it is critical to strike a balance between efficiency and performance. Therefore, many recent approaches efficiently pose XMC as a maximum inner product search between embeddings learned from small encoder-only transformer architectures. In this paper, we address two important aspects in XMC: how to effectively harness larger decoder-only models, and how to exploit visual information while maintaining computational efficiency. We demonstrate that both play a critical role in XMC separately and can be combined for improved performance. We show that a few billion-size decoder can deliver substantial improvements while keeping computational overhead manageable. Furthermore, our Vision-enhanced eXtreme Multi-label Learning framework (ViXML) efficiently integrates foundation vision models by pooling a single embedding per image. This limits computational growth while unlocking multi-modal capabilities. Remarkably, ViXML with small encoders outperforms text-only decoder in most cases, showing that an image is worth billions of parameters. Finally, we present an extension of existing text-only datasets to exploit visual metadata and make them available for future benchmarking. Comprehensive experiments across four public text-only datasets and their corresponding image enhanced versions validate our proposals' effectiveness, surpassing previous state-of-the-art by up to +8.21\\% in P@1 on the largest dataset. ViXML's code is available at https://github.com/DiegoOrtego/vixml.","short_abstract":"Foundation models have revolutionized artificial intelligence across numerous domains, yet their transformative potential remains largely untapped in Extreme Multi-label Classification (XMC). Queries in XMC are associated with relevant labels from extremely large label spaces, where it is critical to strike a balance b...","url_abs":"https://arxiv.org/abs/2511.13189","url_pdf":"https://arxiv.org/pdf/2511.13189v1","authors":"[\"Diego Ortego\",\"Marlon Rodríguez\",\"Mario Almagro\",\"Kunal Dahiya\",\"David Jiménez\",\"Juan C. SanMiguel\"]","published":"2025-11-17T09:52:53Z","proceeding":"cs.CV","tasks":"[\"cs.CV\",\"cs.IR\"]","methods":"[\"Transformer\",\"Language Model\"]","has_code":false,"code_links":[{"ID":606972,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_id":2840505,"paper_url":"https://arxiv.org/abs/2511.13189","paper_title":"Large Language Models Meet Extreme Multi-label Classification: Scaling and Multi-modal Framework","repo_url":"https://github.com/DiegoOrtego/vixml","is_official":false,"mentioned_in_paper":false,"mentioned_in_github":true,"github_stars":0}]}