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This paper addresses a critical gap in tabular embedding models by arguing that standard prediction metrics fail to capture human preference alignment, which is essential for trustworthy similarity search in business systems like PLM. The authors propose a concrete evaluation procedure specifically designed to assess whether these embeddings produce rankings that align with human judgment rather than just optimizing downstream tasks. This work advances the field of machine learning by shifting focus from purely predictive accuracy to user-centric trustworthiness in real-world applications.
Human Preference aligned Tabular Similarity
arXiv:2607.24880v1 Announce Type: cross 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.
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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