PRISM: LLM-Guided Semantic Clustering for High-Precision Topics

2026-04-03

AI Summary

This work addresses the challenge of achieving fine-grained, high-precision topic modeling in narrow domains—particularly distinguishing semantically similar subtopics—under constraints of low cost and interpretability. The authors propose PRISM, a framework that fine-tunes a lightweight sentence encoder using sparse labels generated by a large language model (LLM) and applies threshold-based clustering to partition the embedding space into locally interpretable topic structures. PRISM employs an LLM-guided teacher–student distillation pipeline, requiring only minimal LLM queries and sparse supervision, while also investigating how active sampling strategies influence local embedding geometry. Experiments demonstrate that PRISM significantly outperforms existing local topic models across multiple corpora and even surpasses state-of-the-art large embedding models in clustering quality, offering a compelling balance of efficiency, accuracy, and deployability.

Abstract

In this paper, we propose Precision-Informed Semantic Modeling (PRISM), a structured topic modeling framework combining the benefits of rich representations captured by LLMs with the low cost and interpretability of latent semantic clustering methods. PRISM fine-tunes a sentence encoding model using a sparse set of LLM- provided labels on samples drawn from some corpus of interest. We segment this embedding space with thresholded clustering, yielding clusters that separate closely related topics within some narrow domain. Across multiple corpora, PRISM improves topic separability over state-of-the-art local topic models and even over clustering on large, frontier embedding models while requiring only a small number of LLM queries to train. This work contributes to several research streams by providing (i) a student-teacher pipeline to distill sparse LLM supervision into a lightweight model for topic discovery; (ii) an analysis of the efficacy of sampling strategies to improve local geometry for cluster separability; and (iii) an effective approach for web-scale text analysis, enabling researchers and practitioners to track nuanced claims and subtopics online with an interpretable, locally deployable framework.

Problem

• semantic clustering
• topic modeling
• high-precision topics
• LLM-guided
• local topic separability

Innovation

• semantic clustering
• LLM-guided learning
• topic modeling
• embedding distillation
• thresholded clustering