知识图谱增强的零样本主题分类：多策略比较研究
Knowledge Graph-Enhanced Zero-Shot Topic Classification: A Multi-Strategy Comparative Study
AI总结：
提出零样本多标签主题分类框架，通过知识图谱增强文档表示，实验表明对小型模型有正面影响，对大型模型有负面影响。
摘要：
在没有标注训练数据的情况下进行多标签主题分类是一项具有挑战性的任务，特别是当文档包含复杂的关系信息时。我们提出了一个零样本多标签主题分类框架，并系统地研究了每篇文章的知识图谱增强如何影响其性能。基础框架在没有标注训练数据的情况下对文档中的主题进行分类，有四种变体：仅文章分类、关键词增强分类以及两者的自一致性解码变体。然后，我们为每个基础变体增加每篇文章的知识图谱。该图谱通过类似于KGGen的流水线从输入文档中提取，基于主语-谓语-宾语三元组。我们在十五个大型语言模型和八个跨不同领域的多标签数据集上测试了所有八种方法（四种基础和四种图谱增强）。对于基础框架，关键词增强分类（AK）是表现最好的方法，十五个大型语言模型中有六个超过了句子编码器基线。图谱增强对小型模型有正面影响，对大型模型有负面影响。这表明大型模型已经从预训练中包含了足够的关系信息。此外，自一致性解码变体在任何实验中都没有显示出性能提升，同时计算成本增加了约五倍。
英文摘要：
Multi-label topic classification without labeled training data is a challenging task, specially when documents contain complex relational information. We present a zero-shot multi-label topic classification framework and systematically investigate how per-article knowledge graph augmentation affects its performance. The base framework classifies topics in documents without labeled training data and has four variants: article-only classification, keyword-enhanced classification, and self-consistency decoding variants of both. Then, we augment each base variant with per article knowledge graph. This graph is extracted from the input document through a pipeline similar to KGGen based on subject-predicate-object triples. We test all eight methods, four base and four graph augmented on fifteen LLMs and eight multi-label datasets across different domains. For the base framework, keyword-enhanced classification (AK) is the best performing method, and six out of fifteen LLMs surpass the sentence-encoder baseline. Graph augmentation has positive and negative impacts on small and large models, respectively. This shows that larger models already contain enough relational information from pretraining. Furthermore, the self-consistency decoding variant does not show performance improvements in any experiment while increasing computation costs about fivefold.
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