110 citations · 519 across the 25 of their papers we have counts for
9 papers · 1 filter
Language Rectified Flow: Advancing Diffusion Language Generation with Probabilistic Flows
Shujian Zhang, Lemeng Wu, Chengyue Gong +1
Recent works have demonstrated success in controlling sentence attributes (, sentiment) and structure (, syntactic structure) based on the diffusion language model. A k…
AutoML-GPT: Automatic Machine Learning with GPT
Shujian Zhang, Chengyue Gong, Lemeng Wu +2
AI tasks encompass a wide range of domains and fields. While numerous AI models have been designed for specific tasks and applications, they often require considerable human effort…
Passage-Mask: A Learnable Regularization Strategy for Retriever-Reader Models
Shujian Zhang, Chengyue Gong, Xingchao Liu
Retriever-reader models achieve competitive performance across many different NLP tasks such as open question answering and dialogue conversations. In this work, we notice these mo…
ALLSH: Active Learning Guided by Local Sensitivity and Hardness
Shujian Zhang, Chengyue Gong, Xingchao Liu +3
Active learning, which effectively collects informative unlabeled data for annotation, reduces the demand for labeled data. In this work, we propose to retrieve unlabeled samples w…
Learning with Different Amounts of Annotation: From Zero to Many Labels
Shujian Zhang, Chengyue Gong, Eunsol Choi
Training NLP systems typically assumes access to annotated data that has a single human label per example. Given imperfect labeling from annotators and inherent ambiguity of langua…
Knowing More About Questions Can Help: Improving Calibration in Question Answering
Shujian Zhang, Chengyue Gong, Eunsol Choi
We study calibration in question answering, estimating whether model correctly predicts answer for each question. Unlike prior work which mainly rely on the model's confidence scor…
Capturing Label Distribution: A Case Study in NLI
Shujian Zhang, Chengyue Gong, Eunsol Choi
We study estimating inherent human disagreement (annotation label distribution) in natural language inference task. Post-hoc smoothing of the predicted label distribution to match…
Sentence-wise Smooth Regularization for Sequence to Sequence Learning
Chengyue Gong, Xu Tan, Di He +1
Maximum-likelihood estimation (MLE) is widely used in sequence to sequence tasks for model training. It uniformly treats the generation/prediction of each target token as multi-cla…
FRAGE: Frequency-Agnostic Word Representation
Chengyue Gong, Di He, Xu Tan +3
Continuous word representation (aka word embedding) is a basic building block in many neural network-based models used in natural language processing tasks. Although it is widely a…