LiMA reformulates attribution as submodular subset selection and uses bidirectional greedy search to identify minimal important regions, reporting 36.3% better insertion and 39.6% better deletion scores than prior methods on eight foundation models.
Caltech-UCSD Birds 200. Technical Report CNS-TR- 2010–001,
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Less is More: Efficient Black-box Attribution via Minimal Interpretable Subset Selection
LiMA reformulates attribution as submodular subset selection and uses bidirectional greedy search to identify minimal important regions, reporting 36.3% better insertion and 39.6% better deletion scores than prior methods on eight foundation models.