Abstract
Hierarchical text classification (HTC) assigns documents to multiple levels of a pre-defined taxonomy. Automated patent subject classification represents one of the hardest HTC scenarios because of domain knowledge difficulty and a huge number of labels. Prior approaches only output a flat label set, which offers little insight into the reason behind predictions. Therefore, we propose Reasoning for Hierarchical Classification (RHC), a novel framework that reformulates HTC as a step-by-step reasoning task to sequentially deduce hierarchical labels. RHC trains large language models (LLMs) in two stages: a cold-start stage that aligns outputs with chain-of-thought (CoT) reasoning format and a reinforcement learning (RL) stage to enhance multi-step reasoning ability. RHC demonstrates four advantages in our experiments. (1) Effectiveness: RHC surpasses previous baselines and outperforms the supervised fine-tuning counterparts by approximately 3% in accuracy and macro F1. (2) Explainability: RHC produces natural-language justifications before prediction to facilitate human inspection. (3) Scalability: RHC scales favorably with model size with larger gains compared to standard fine-tuning. (4) Applicability: Beyond patents, we further demonstrate that RHC achieves state-of-the-art performance on other widely used HTC benchmarks, which highlights its broad applicability.
Abstract (translated)
层次文本分类（HTC）将文档分配到预定义的分类体系结构中的多个层级。自动化专利主题分类代表了最复杂的HTC场景之一，因为其需要专业的领域知识，并且标签数量巨大。之前的方法只输出扁平化的标签集合，这无法提供预测背后的理由。因此，我们提出了一种新的框架——用于层次分类的理由推理（RHC），它将HTC重新表述为一个逐步推理任务，以顺序推导出层级标签。RHC通过两个阶段训练大型语言模型（LLM）：第一阶段是对齐链式思维（CoT）推理格式的冷启动阶段；第二阶段是增强多步推理能力的强化学习（RL）阶段。 在我们的实验中，RHC展示了四个优势： 1. **有效性**：与之前的基准方法相比，RHC的表现更佳，并且其监督微调版本的准确性提高了大约3%。 2. **解释性**：在预测前生成自然语言的理由，有助于人工审核和理解模型的决策过程。 3. **可扩展性**：随着模型大小的增长，RHC表现出更好的性能提升效果，相比标准微调方法有更大的收益。 4. **适用性**：除了专利分类之外，我们在其他广泛使用的HTC基准上也展示了RHC能达到最先进的表现水平，这突显了其广泛的适用性和通用性。
URL
https://arxiv.org/abs/2510.07167
