VibeSpace: Automatic Generation of Data and Vector Embeddings for Arbitrary Domains and Cross-domain Mappings using LLMs
ACMMM We present VibeSpace, a novel method for the fully unsupervised construction of interpretable embedding spaces applicable to arbitrary domains. Our approach automates costly data acquisition by leveraging the knowledge embedded in large language models (LLMs), facilitating similarity assessments between entities for meaningful positioning within vector spaces, while also enabling intelligent mappings between vector space representations of disparate domains through a novel form of cross-domain similarity analysis. First, we demonstrate that our data collection methodology yields comprehensive and rich datasets across multiple domains, including songs, books, and movies. We validate the reliability of the automatically generated data via cross-checks with domain-specific catalogues. Second, we show that our method generates single-domain embedding spaces that are separable by domain-specific features, providing a robust foundation for classification tasks, recommendation systems, and other downstream applications. These spaces can be interactively queried for semantic information about different regions in embedding spaces. Lastly, by exploiting the unique capabilities of current state-of-the-art large language models, we produce cross-domain mappings that capture contextual relationships between heterogeneous entities that may not be attainable through traditional methods. This approach facilitates the creation of embedding spaces of any domain, which circumvents the need to collect and calibrate sensitive user data and provides deeper insights and better interpretations of multi-domain data.
@inproceedings{Freud2025vibespace, author = {Freud, K. and Collins, D. and Sampaio Neto, D.D. and Stevens, G.}, title = {VibeSpace: Automatic Generation of Data and Vector Embeddings for Arbitrary Domains and Cross-domain Mappings using LLMs}, year = {2025}, publisher = {Association for Computing Machinery}, doi = {10.1145/3746027.3755830}, pages = {6335-6342}, numpages = {8}, keywords = {data mining, large language model distillation, recommendation}, series = {MM `25}, }