Human Preference aligned Tabular Similarity
Researchers argue that standard tabular embedding metrics fail to capture human preference, necessitating new alignment methods for similarity search.
Task-agnostic tabular embeddings are widely used in business systems like PLM, but current approaches prioritize prediction over human-aligned similarity. The authors suggest that downstream metrics are insufficient for evaluating trustworthiness in similarity search, proposing a shift toward human-preference alignment.
