What is the throughput and inference efficiency trade-off of GraphMETRO's node-based alignment mechanism versu

Abstract

Abstract: The discovery of deep, steerable taxonomies in large text corpora is currently restricted by a trade-off between the surface-level efficiency of topic models and the prohibitive, non-scalable assignment costs of LLM-integrated frameworks. We introduce textbf\LogiPart\, a scalable, hypothesis-first framework for building interpretable hierarchical partitions that decouples hierarchy growth from expensive full-corpus LLM conditioning. LogiPart utilizes locally hosted LLMs on compact, embedding-aware samples to generate concise natural-language taxonomic predicates. These predicates are then eva

Research Question

What is the throughput and inference efficiency trade-off of GraphMETRO's node-based alignment mechanism versus full Bayesian inference on large-scale language model benchmarks (e.g., GLUE, SuperGLUE) under covariate shift?

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