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cs.CL2023
Predicting generalization performance with correctness discriminators
Yuekun Yao, Alexander Koller
The ability to predict an NLP model's accuracy on unseen, potentially out-of-distribution data is a prerequisite for trustworthiness. We present a novel model that establishes uppe…
cs.CL2023
SLOG: A Structural Generalization Benchmark for Semantic Parsing
Bingzhi Li, Lucia Donatelli, Alexander Koller +3
The goal of compositional generalization benchmarks is to evaluate how well models generalize to new complex linguistic expressions. Existing benchmarks often focus on lexical gene…