TaxoAdapt: Aligning LLM-Based Multidimensional Taxonomy Construction to
  Evolving Research Corpora
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.
684c2c6c3b733ba333687248
Priyanka Kargupta
claimed_verified
2025-06-13T14:59:14.479Z
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/avatars/08b23ccfa1f3bede6ade5a1aef06931d.svg
Priyanka Kargupta
user
pkargupta
684c2c6c3b733ba333687249
Nan Zhang
684c2c6c3b733ba33368724a
Yunyi Zhang
684c2c6c3b733ba33368724b
Rui Zhang
684c2c6c3b733ba33368724c
Prasenjit Mitra
684c2c6c3b733ba33368724d
Jiawei Han
TaxoAdapt: Aligning LLM-Based Multidimensional Taxonomy Construction to
  Evolving Research Corpora
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.
TaxoAdapt dynamically adapts an LLM-generated taxonomy for scientific literature across multiple dimensions, improving granularity and coherence compared to existing methods.
LLM-generated taxonomy iterative hierarchical classification computer science conferences granularity-preserving coherence