Human Preference aligned Tabular Similarity

Abstract:Task-agnostic tabular embeddings are increasingly used for similarity search in real-world business systems such as Product Lifecycle Management (PLM). However, leading embedding approaches are optimized primarily for prediction tasks - not for producing human preference aligned similarity rankings. We argue that standard downstream metrics are insufficient to fully assess embedding trustworthiness for similarity search and that human preference aligned evaluation is a necessary and currently missing component. We present a concrete evaluation procedure and illustrate the problem through a PLM use case.

Machine Learning (cs.LG); Artificial Intelligence (cs.AI)

arXiv:2607.24880 [cs.LG]

(or arXiv:2607.24880v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2607.24880 Focus to learn more arXiv-issued DOI via DataCite

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