16 citations · 43 across the 13 of their papers we have counts for
9 papers · 1 filter
Evaluating the Factuality of Zero-shot Summarizers Across Varied Domains
Sanjana Ramprasad, Kundan Krishna, Zachary C Lipton +1
Recent work has shown that large language models (LLMs) are capable of generating summaries zero-shot (i.e., without explicit supervision) that, under human assessment, are often c…
GenAudit: Fixing Factual Errors in Language Model Outputs with Evidence
Kundan Krishna, Sanjana Ramprasad, Prakhar Gupta +3
LLMs can generate factually incorrect statements even when provided access to reference documents. Such errors can be dangerous in high-stakes applications (e.g., document-grounded…
USB: A Unified Summarization Benchmark Across Tasks and Domains
Kundan Krishna, Prakhar Gupta, Sanjana Ramprasad +3
While the NLP community has produced numerous summarization benchmarks, none provide the rich annotations required to simultaneously address many important problems related to cont…
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…
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…
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…
Does Pretraining for Summarization Require Knowledge Transfer?
Kundan Krishna, Jeffrey Bigham, Zachary C. Lipton
Pretraining techniques leveraging enormous datasets have driven recent advances in text summarization. While folk explanations suggest that knowledge transfer accounts for pretrain…
Reinforced Rewards Framework for Text Style Transfer
Abhilasha Sancheti, Kundan Krishna, Balaji Vasan Srinivasan +1
Style transfer deals with the algorithms to transfer the stylistic properties of a piece of text into that of another while ensuring that the core content is preserved. There has b…
Generating SOAP Notes from Doctor-Patient Conversations Using Modular Summarization Techniques
Kundan Krishna, Sopan Khosla, Jeffrey P. Bigham +1
Following each patient visit, physicians draft long semi-structured clinical summaries called SOAP notes. While invaluable to clinicians and researchers, creating digital SOAP note…