FLARE scores embedding models labellessly via normalized log-likelihood, achieving 0.90 Spearman correlation with supervised benchmarks and stable performance in dimensions over 3500 where prior methods collapse.
Laurent Dinh, Jascha Sohl-Dickstein, and Samy Ben- gio
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FLARE: Task-agnostic embedding model evaluation through a normalization process
FLARE scores embedding models labellessly via normalized log-likelihood, achieving 0.90 Spearman correlation with supervised benchmarks and stable performance in dimensions over 3500 where prior methods collapse.