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cs.AI2023
Automating the Generation of Prompts for LLM-based Action Choice in PDDL Planning
Katharina Stein, Daniel Fišer, Jörg Hoffmann +1
Large language models (LLMs) have revolutionized a large variety of NLP tasks. An active debate is to what extent they can do reasoning and planning. Prior work has assessed the la…
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…