TaxoAdapt：将基于大语言模型的多维分类体系构建与动态研究语料库对齐

TaxoAdapt: Aligning LLM-Based Multidimensional Taxonomy Construction to Evolving Research Corpora

June 12, 2025

作者: Priyanka Kargupta, Nan Zhang, Yunyi Zhang, Rui Zhang, Prasenjit Mitra, Jiawei Han

cs.AI

摘要

科学领域的快速发展给科学文献的组织与检索带来了挑战。尽管专家精心构建的分类体系传统上满足了这一需求，但这一过程耗时且成本高昂。此外，现有的自动分类构建方法要么（1）过度依赖特定语料库，牺牲了普适性，要么（2）过分倚重大型语言模型（LLMs）预训练数据集中的通用知识，往往忽视了科学领域动态演变的特性。同时，这些方法未能充分考虑科学文献的多维性，即单篇研究论文可能涉及多个维度（如方法论、新任务、评估指标、基准测试）。为填补这些空白，我们提出了TaxoAdapt框架，该框架能够动态调整LLM生成的分类体系，使其适应给定语料库的多维度特征。TaxoAdapt通过迭代的层次分类，依据语料库的主题分布扩展分类的广度和深度。我们通过展示其在多年间多个计算机科学会议上的卓越表现，证明了其构建并捕捉科学领域演变的能力。作为一种多维方法，TaxoAdapt生成的分类体系在LLM评估下，比最具竞争力的基线方法在粒度保持上提升了26.51%，在连贯性上提升了50.41%。

English

The rapid evolution of scientific fields introduces challenges in organizing and retrieving scientific literature. While expert-curated taxonomies have traditionally addressed this need, the process is time-consuming and expensive. Furthermore, recent automatic taxonomy construction methods either (1) over-rely on a specific corpus, sacrificing generalizability, or (2) depend heavily on the general knowledge of large language models (LLMs) contained within their pre-training datasets, often overlooking the dynamic nature of evolving scientific domains. Additionally, these approaches fail to account for the multi-faceted nature of scientific literature, where a single research paper may contribute to multiple dimensions (e.g., methodology, new tasks, evaluation metrics, benchmarks). To address these gaps, we propose TaxoAdapt, a framework that dynamically adapts an LLM-generated taxonomy to a given corpus across multiple dimensions. TaxoAdapt performs iterative hierarchical classification, expanding both the taxonomy width and depth based on corpus' topical distribution. We demonstrate its state-of-the-art performance across a diverse set of computer science conferences over the years to showcase its ability to structure and capture the evolution of scientific fields. As a multidimensional method, TaxoAdapt generates taxonomies that are 26.51% more granularity-preserving and 50.41% more coherent than the most competitive baselines judged by LLMs.