arXiv:2603.10619v2 Announce Type: replace Abstract: The recent success of large pre-trained language models (PLMs) has motivated their integration into topic modeling. However, PLM-augmented topic models differ from classical co-occurrence models such as Latent Dirichlet Allocation (LDA) not only in performance, but also in the type of semantic structure they capture. We formalize this distinction along two psycholinguistic axes: thematic relatedness (dog/bone) and taxonomic similarity (dog/wolf). To measure both axes over topic words, we construct a large synthetic benchmark of word pairs usi
The proliferation of sophisticated large pre-trained language models necessitates a deeper understanding of their semantic capabilities compared to traditional topic modeling approaches.
Distinguishing between similarity and relatedness in AI topic models is crucial for developing more nuanced and accurate AI systems, impacting interpretability and application design.
This research provides a formalized method and benchmark for evaluating how different topic models capture semantic structures, potentially leading to improved model selection and development.
- · AI researchers
- · NLP developers
- · Companies relying on semantic search
Improved understanding of the semantic representations within different AI models.
Development of next-generation topic models that explicitly control for similarity versus relatedness.
More robust and explainable AI applications that better distinguish nuances in human language concepts.
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