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20232026
most citedAn Empirical Study of Pre-trained Model Selection for Out-of-Distribution Generalization and Calibration

2 citations · 3 across the 9 of their papers we have counts for

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cs.CL2024★ 1 cited

Augmenting NER Datasets with LLMs: Towards Automated and Refined Annotation

Yuji Naraki, Ryosuke Yamaki, Yoshikazu Ikeda +4

In the field of Natural Language Processing (NLP), Named Entity Recognition (NER) is recognized as a critical technology, employed across a wide array of applications. Traditional…

cs.LG2024

Towards Understanding Variants of Invariant Risk Minimization through the Lens of Calibration

Kotaro Yoshida, Hiroki Naganuma

Machine learning models traditionally assume that training and test data are independently and identically distributed. However, in real-world applications, the test distribution o…