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LLM-Enhanced Dual-Branch Learning for Large-Scale Multi-Label Text Classification

Paper recorded by Signals 4 on 2026-09-11 in cs.CL. Abstract reproduced from arXiv; link to the original below.

Published 2026-09-11 on arXiv · recorded by Signals 4 on 2026-09-14

Category: cs.CL · 自然语言处理 · first seen 2026-09-14

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 sing

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#58 most recent of 186 cs.CL papers we have recorded · ↑ newer: Fewer Words, Not Fewer Tokens: Measuring the Sanskrit Tokenization Pen · ↓ older: Parameter-Efficient Retrievers for Polish and European Languages
Cite this page: LLM-Enhanced Dual-Branch Learning for Large-Scale Multi-Label Text Classification: the #58 most recent of 186 cs.CL papers we have recorded (as of 2026-09-11). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/llm-enhanced-dual-branch-learning-for-large-scale-multi-label-text-classificatio.html
Free to quote with attribution to “Signals 4 (Signals API)”. Machine-readable: papers.json
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