4 papers
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…
Ordinal Gates, Cardinal Bets: Matching LLM Confidence to the Financial Decision Operator
Rayansh Singh, Sara Rezaeimanesh
LLM confidence scores are not independently deployable objects: their decision value depends on the downstream operator and exposure controller that consume them. Monotone recalibr…
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…
Large Language Models for Persian English Idiom Translation
Sara Rezaeimanesh, Faezeh Hosseini, Yadollah Yaghoobzadeh
Large language models (LLMs) have shown superior capabilities in translating figurative language compared to neural machine translation (NMT) systems. However, the impact of differ…