@inproceedings{xu-etal-2026-structured,
    title = "Structured Confidence{--}Guided Online Adaptation for {LLM}-based Multi-Label Classification",
    author = "Xu, Pengyu  and
      Hou, JingRen  and
      Jing, Liping  and
      Yu, Jian",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.findings-acl.822/",
    pages = "16671--16686",
    ISBN = "979-8-89176-395-1",
    abstract = "Large language models (LLMs) enable zero-shot and few-shot multi-label text classification via in-context learning, yet most approaches perform static inference and degrade under streaming test data due to distribution shift and long-tail labels. We study online test-time adaptation for LLM-based multi-label generation without any parameter updates, and identify two bottlenecks: (1) standard generation probabilities provide unreliable confidence because they ignore label competition at key decoding branches; (2) naive confidence-based caching overfits to frequent and easy examples, reducing label coverage and diversity. We propose SCOTTA, a structured confidence-guided online adaptation framework. SCOTTA introduces Label-set Local Likelihood Ratio (L3R), a label-level confidence measure that compares a target label against its valid competitors at critical decision positions. Using L3R as a unified signal, SCOTTA maintains an in-context exemplar cache via streaming submodular maximization, balancing label coverage, semantic diversity, and sample quality under a fixed context budget. Across four benchmarks, SCOTTA consistently improves Micro-F1 and Macro-F1 over strong LLM and non-LLM baselines, with the largest gains on long-tail labels."
}