TaxoAlign: Scholarly Taxonomy Generation Using Language Models

Taxonomies play a crucial role in helping researchers structure and navigate knowledge in a hierarchical manner. They also form an important part in the creation of comprehensive literature surveys. The existing approaches to automatic survey generation do not compare the structure of the generated surveys with those written by human experts. To address this gap, we present our own method for automated taxonomy creation that can bridge the gap between human-generated and automaticallycreated taxonomies. For this purpose, we create the CS-TAXOBENCH benchmark which consists of 460 taxonomies that have been extracted from human-written survey papers. We also include an additional test set of 80 taxonomies curated from conference survey papers. We propose TAXOALIGN, a threephase topic-based instruction-guided method for scholarly taxonomy generation. Additionally, we propose a stringent automated evaluation framework that measures the structural alignment and semantic coherence of automatically generated taxonomies in comparison to those created by human experts. We evaluate our method and various baselines on CS-TAXOBENCH, using both automated evaluation metrics and human evaluation studies. The results show that TAXOALIGN consistently surpasses the baselines on nearly all metrics. The code and data can be found at https: //github.com/AvishekLahiri/TaxoAlign . Gold Standard Taxonomy Generated Taxonomy Human Image Generation : A Comprehensive Survey |--DATA-DRIVEN METHODS ON HUMAN IMAGE GENERATION | |--Method Taxonomy Based on Fundamental Models | |--Method Taxonomy Based on Task Settings | +--Main Components in Data-Driven Methods |--HYBRID METHODS |--KNOWLEDGE-GUIDED METHODS ON | | HUMAN IMAGE GENERATION | |--Fundamental Models of Knowledge-Guided Methods | |--Pixel Warping Pipeline | +--Virtual Rendering Pipeline |--APPLICATIONS