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Soft-Prompted Semantic Normalization for Unsupervised Analysis of the Scientific Literature

Mapping thematic structure in large scientific corpora enables the systematic analysis of research trends and conceptual organization. This work presents an unsupervised framework that leverages large language models (LLMs) as fixed semantic inference operators guided by structured soft prompts. The...

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Principais autores: Ivan Malashin, Dmitry Martysyuk, Vadim Tynchenko, Andrei Gantimurov, Vladimir Nelyub, Aleksei Borodulin
Formato: Artigo
Idioma:Inglês
Publicado em: MDPI AG 2026-03-01
coleção:Machine Learning and Knowledge Extraction
Assuntos:
large language models
scientific literature analysis
semantic normalization
unsupervised clustering
research trend discovery
Acesso em linha:https://www.mdpi.com/2504-4990/8/3/63
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