41 citations · 98 across the 15 of their papers we have counts for
15 papers
XGen-7B Technical Report
Erik Nijkamp, Tian Xie, Hiroaki Hayashi +22
Large Language Models (LLMs) have become ubiquitous across various domains, transforming the way we interact with information and conduct research. However, most high-performing LL…
Attention-based 3D CNN with Multi-layer Features for Alzheimer's Disease Diagnosis using Brain Images
Yanteng Zhang, Qizhi Teng, Xiaohai He +4
Structural MRI and PET imaging play an important role in the diagnosis of Alzheimer's disease (AD), showing the morphological changes and glucose metabolism changes in the brain re…
Marvista: Exploring the Design of a Human-AI Collaborative News Reading Tool
Xiang 'Anthony' Chen, Chien-Sheng Wu, Lidiya Murakhovs'ka +4
We explore the design of Marvista -- a human-AI collaborative tool that employs a suite of natural language processing models to provide end-to-end support for reading online news…
OneAligner: Zero-shot Cross-lingual Transfer with One Rich-Resource Language Pair for Low-Resource Sentence Retrieval
Tong Niu, Kazuma Hashimoto, Yingbo Zhou +1
Aligning parallel sentences in multilingual corpora is essential to curating data for downstream applications such as Machine Translation. In this work, we present OneAligner, an a…
MixQG: Neural Question Generation with Mixed Answer Types
Lidiya Murakhovs'ka, Chien-Sheng Wu, Philippe Laban +3
Asking good questions is an essential ability for both human and machine intelligence. However, existing neural question generation approaches mainly focus on the short factoid typ…
Explaining and Improving Model Behavior with k Nearest Neighbor Representations
Nazneen Fatema Rajani, Ben Krause, Wengpeng Yin +3
Interpretability techniques in NLP have mainly focused on understanding individual predictions using attention visualization or gradient-based saliency maps over tokens. We propose…
Unsupervised Paraphrasing with Pretrained Language Models
Tong Niu, Semih Yavuz, Yingbo Zhou +3
Paraphrase generation has benefited extensively from recent progress in the designing of training objectives and model architectures. However, previous explorations have largely fo…
CoCo: Controllable Counterfactuals for Evaluating Dialogue State Trackers
Shiyang Li, Semih Yavuz, Kazuma Hashimoto +6
Dialogue state trackers have made significant progress on benchmark datasets, but their generalization capability to novel and realistic scenarios beyond the held-out conversations…
Char2Subword: Extending the Subword Embedding Space Using Robust Character Compositionality
Gustavo Aguilar, Bryan McCann, Tong Niu +3
Byte-pair encoding (BPE) is a ubiquitous algorithm in the subword tokenization process of language models as it provides multiple benefits. However, this process is solely based on…
AvgOut: A Simple Output-Probability Measure to Eliminate Dull Responses
Tong Niu, Mohit Bansal
Many sequence-to-sequence dialogue models tend to generate safe, uninformative responses. There have been various useful efforts on trying to eliminate them. However, these approac…
Automatically Learning Data Augmentation Policies for Dialogue Tasks
Tong Niu, Mohit Bansal
Automatic data augmentation (AutoAugment) (Cubuk et al., 2019) searches for optimal perturbation policies via a controller trained using performance rewards of a sampled policy on…
Deleter: Leveraging BERT to Perform Unsupervised Successive Text Compression
Tong Niu, Caiming Xiong, Richard Socher
Text compression has diverse applications such as Summarization, Reading Comprehension and Text Editing. However, almost all existing approaches require either hand-crafted feature…
Adversarial Over-Sensitivity and Over-Stability Strategies for Dialogue Models
Tong Niu, Mohit Bansal
We present two categories of model-agnostic adversarial strategies that reveal the weaknesses of several generative, task-oriented dialogue models: Should-Not-Change strategies tha…
Polite Dialogue Generation Without Parallel Data
Tong Niu, Mohit Bansal
Stylistic dialogue response generation, with valuable applications in personality-based conversational agents, is a challenging task because the response needs to be fluent, contex…
Detecting Linguistic Characteristics of Alzheimer's Dementia by Interpreting Neural Models
Sweta Karlekar, Tong Niu, Mohit Bansal
Alzheimer's disease (AD) is an irreversible and progressive brain disease that can be stopped or slowed down with medical treatment. Language changes serve as a sign that a patient…