大模型
arxiv.org ·
LLM 增强的双分支学习用于大规模多标签文本分类
论文提出 DualMLC 双分支框架：同一文档分别经自回归 decoder-only 语言模型与双向 encoder 处理，各分支独立在共享标签空间上估计相关性分数，再通过 late logit fusion 融合两个分数向量，使共享证据强化相关标签、分支特有证据互补。作者称在三个常用大规模多标签文本分类基准上取得 SOTA，消融实验表明异构预测器融合的排序效果优于任一单分支，并公开了源代码。
AI推荐理由 arXiv 正式提交的 cs.CL 论文，摘要明确给出方法（DualMLC 双分支：自回归 decoder-only 模型 + 双向 encoder，late logit fusion）与结论（三个大规模多标签文本分类基准上达到 SOTA，消融显示异构预测器融合优于单分支），并声明开源代码，事实与证据较清晰。局限在于仅依据摘要，未提供具体数据集、指标数值与代码链接细节，且多标签文本分类属相对成熟方向，对 Agent/应用层的直接外溢有限，故兴趣分中等。
72
正文
Computer Science > Computation and Language
arXiv:2609.12915 (cs)
[Submitted on 11 Sep 2026]
Title:LLM-Enhanced Dual-Branch Learning for Large-Scale Multi-Label Text Classification
Authors:Hui Ye, Jing Zhang, Xiulong Yang, Rajshekhar Sunderraman
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Abstract:Large-scale multi-label text classification assigns a small subset of relevant labels to each document from a vocabulary containing thousands or tens of thousands of candidate labels. Although pretrained language models have improved semantic text representations, most representation-based approaches center their prediction pipelines on a primary encoder or combine auxiliary features within a single ranker. The complementarity between heterogeneous language models therefore remains insufficiently explored. We propose DualMLC, a dual-branch framework that processes the same document through an autoregressive decoder-only language model and a bidirectional encoder. Each branch maintains its own representation pathway and independently estimates relevance scores over the shared label space. DualMLC combines the two score vectors through late logit fusion, allowing shared evidence to reinforce relevant labels and branch-specific evidence to compensate for limitations in the other branch's representation. DualMLC achieves state-of-the-art results on three widely used large-scale multi-label text classification benchmarks. Ablation results further confirm that integrating the heterogeneous predictors produces stronger rankings than either branch alone. The source code is publicly available at this https URL.
Subjects:
Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
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arXiv:2609.12915 [cs.CL]
(or
arXiv:2609.12915v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2609.12915
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arXiv-issued DOI via DataCite (pending registration)
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From: Hui Ye [view email]
[v1]
Fri, 11 Sep 2026 14:42:16 UTC (174 KB)
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