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cs.CL2024
PertEval: Unveiling Real Knowledge Capacity of LLMs with Knowledge-Invariant Perturbations
Jiatong Li, Renjun Hu, Kunzhe Huang +5
Expert-designed close-ended benchmarks are indispensable in assessing the knowledge capacity of large language models (LLMs). Despite their widespread use, concerns have mounted re…
cs.LG2024★ 1 cited
Arithmetic Feature Interaction Is Necessary for Deep Tabular Learning
Yi Cheng, Renjun Hu, Haochao Ying +3
Until recently, the question of the effective inductive bias of deep models on tabular data has remained unanswered. This paper investigates the hypothesis that arithmetic feature…