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ARXIV:2604.03180 · TOPIC MODELING WITH LLMS · SUBMITTED 06 APR · 20:16 UTC · FRESHNESS UNKNOWN
ARXIV:2604.03180TOPIC MODELING WITH LLMSSUBMITTED 06 APR · 20:16 UTCFRESHNESS UNKNOWNConnor Douglas · Utkucan Balci · Joseph Aylett-Bullock · arXiv
A topic modeling framework that uses LLMs to guide semantic clustering for precise topic discovery and analysis.
Opportunity summary
Pain A topic modeling framework that uses LLMs to guide semantic clustering for precise topic discovery and analysis.
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A topic modeling framework that uses LLMs to guide semantic clustering for precise topic discovery and analysis. PRISM fine-tunes a sentence encoding model using a sparse set of LLM- provided labels on samples drawn…
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.…
ScienceToStartup currently rates this 5.0/10 on the public viability pass. 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…
Topic Modeling with LLMs moved forward this cycle; last verified April 2026. Public score 5.0/10.
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A topic modeling framework that uses LLMs to guide semantic clustering for precise topic discovery and analysis.
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10.48550/arXiv.2604.03180A topic modeling framework that uses LLMs to guide semantic clustering for precise topic discovery and analysis.
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.
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PROBLEM
A topic modeling framework that uses LLMs to guide semantic clustering for precise topic discovery and analysis. PRISM fine-tunes a sentence encoding model using a sparse set of LLM- provided labels on samples drawn from some corpus of interest.
METHOD
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...
RESULT
ScienceToStartup currently rates this 5.0/10 on the public viability pass. 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...
WHY NOW
Topic Modeling with LLMs moved forward this cycle; last verified April 2026. Public score 5.0/10.
Abstract-backed public claims while anchored extraction refreshes.
A topic modeling framework that uses LLMs to guide semantic clustering for precise topic discovery and analysis. PRISM fine-tunes a sentence encoding model using a sparse set of LLM- provided labels on samples drawn from some corpus of interest.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
ScienceToStartup currently rates this 5.0/10 on the public viability pass. 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.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Topic Modeling with LLMs moved forward this cycle; last verified April 2026. Public score 5.0/10.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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A topic modeling framework that uses LLMs to guide semantic clustering for precise topic discovery and analysis.
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Topic Modeling with LLMs
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Commercial read
5.0/10 public viability
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partial
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