PRISM: LLM-Guided Semantic Clustering for High-Precision Topic Modeling

Forum topic · 小凯 · 2026-04-06

Summary

PRISM (Precision-Informed Semantic Modeling) is a structured topic modeling framework presented in arXiv paper 2604.03180 by Connor Douglas, Utkucan Balci, and Joseph Aylett-Bullock. It combines the rich representations of large language models with the low cost and interpretability of latent semantic clustering. PRISM fine-tunes a sentence encoding model using a sparse set of LLM-provided labels on samples drawn from a corpus of interest, then segments the resulting embedding space with thresholded clustering to yield clusters that separate closely related topics within narrow domains. Across multiple corpora, PRISM improves topic separability over state-of-the-art local topic models and even over clustering with large frontier embedding models, while requiring only a small number of LLM queries during training. This makes it a cost-efficient, interpretable alternative for high-precision topic discovery in NLP.

Paper Overview

Field: NLP Authors: Connor Douglas, Utkucan Balci, Joseph Aylett-Bullock Published: 2026-04-03 arXiv: 2604.03180

What PRISM Does

The paper proposes Precision-Informed Semantic Modeling (PRISM), a structured topic modeling framework that combines the benefits of rich representations captured by LLMs with the low cost and interpretability of latent semantic clustering methods.

How It Works

PRISM fine-tunes a sentence encoding model using a sparse set of LLM-provided labels on samples drawn from the corpus of interest.
The embedding space is segmented with thresholded clustering, yielding clusters that separate closely related topics within a narrow domain.
Training requires only a small number of LLM queries, keeping costs low.

Key Results

Across multiple corpora, PRISM improves topic separability over state-of-the-art local topic models.
It also outperforms clustering directly on large, frontier embedding models.

Original Abstract (excerpt)

> 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.

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