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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Auteurs principaux:	Ivan Malashin, Dmitry Martysyuk, Vadim Tynchenko, Andrei Gantimurov, Vladimir Nelyub, Aleksei Borodulin
Format:	Artigo
Langue:	Inglês
Publié:	MDPI AG 2026-03-01
Collection:	Machine Learning and Knowledge Extraction
Sujets:	large language models scientific literature analysis semantic normalization unsupervised clustering research trend discovery
Accès en ligne:	https://www.mdpi.com/2504-4990/8/3/63
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