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cs.LG2026
INCONVENIENT: A Benchmark to Assess LLM Generalization Capabilities from Convenience Samples
Sara Rezaeimanesh, Chad Klochko, Kundan Thind +1
Machine learning models for tabular prediction typically assume that training data are representative of the population on which they will be deployed. In practice, however, data a…
cs.CL2026
What Language Models Know But Don't Say: Non-Generative Prior Extraction for Generalization
Sara Rezaeimanesh, Kundan Thind, Farzan Siddiqui +1
In domains like medicine and finance, large-scale labeled data is costly and often unavailable, leading to models trained on small datasets that struggle to generalize to real-worl…