16 citations · 43 across the 13 of their papers we have counts for
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cs.CL2022
Improving the Robustness of Summarization Models by Detecting and Removing Input Noise
Kundan Krishna, Yao Zhao, Jie Ren +4
The evaluation of abstractive summarization models typically uses test data that is identically distributed as training data. In real-world practice, documents to be summarized may…
cs.CL2022★ 12 cited
Out-of-Distribution Detection and Selective Generation for Conditional Language Models
Jie Ren, Jiaming Luo, Yao Zhao +4
Machine learning algorithms typically assume independent and identically distributed samples in training and at test time. Much work has shown that high-performing ML classifiers c…
cs.CL2022★ 7 cited
Downstream Datasets Make Surprisingly Good Pretraining Corpora
Kundan Krishna, Saurabh Garg, Jeffrey P. Bigham +1
For most natural language processing tasks, the dominant practice is to finetune large pretrained transformer models (e.g., BERT) using smaller downstream datasets. Despite the suc…