414 citations · 1.4k across the 41 of their papers we have counts for
42 papers
APOLLO: A Simple Approach for Adaptive Pretraining of Language Models for Logical Reasoning
Soumya Sanyal, Yichong Xu, Shuohang Wang +5
Logical reasoning of text is an important ability that requires understanding the information present in the text, their interconnections, and then reasoning through them to infer…
Empowering Language Models with Knowledge Graph Reasoning for Question Answering
Ziniu Hu, Yichong Xu, Wenhao Yu +5
Answering open-domain questions requires world knowledge about in-context entities. As pre-trained Language Models (LMs) lack the power to store all required knowledge, external kn…
Retrieval Augmentation for Commonsense Reasoning: A Unified Approach
Wenhao Yu, Chenguang Zhu, Zhihan Zhang +4
A common thread of retrieval-augmented methods in the existing literature focuses on retrieving encyclopedic knowledge, such as Wikipedia, which facilitates well-defined entity and…
Prompting GPT-3 To Be Reliable
Chenglei Si, Zhe Gan, Zhengyuan Yang +4
Large language models (LLMs) show impressive abilities via few-shot prompting. Commercialized APIs such as OpenAI GPT-3 further increase their use in real-world language applicatio…
Generate rather than Retrieve: Large Language Models are Strong Context Generators
Wenhao Yu, Dan Iter, Shuohang Wang +6
Knowledge-intensive tasks, such as open-domain question answering (QA), require access to a large amount of world or domain knowledge. A common approach for knowledge-intensive tas…
Language Models with Image Descriptors are Strong Few-Shot Video-Language Learners
Zhenhailong Wang, Manling Li, Ruochen Xu +10
The goal of this work is to build flexible video-language models that can generalize to various video-to-text tasks from few examples, such as domain-specific captioning, question…
AdaPrompt: Adaptive Model Training for Prompt-based NLP
Yulong Chen, Yang Liu, Li Dong +4
Prompt-based learning, with its capability to tackle zero-shot and few-shot NLP tasks, has gained much attention in community. The main idea is to bridge the gap between NLP downst…
CLIP-Event: Connecting Text and Images with Event Structures
Manling Li, Ruochen Xu, Shuohang Wang +6
Vision-language (V+L) pretraining models have achieved great success in supporting multimedia applications by understanding the alignments between images and text. While existing v…
MLP Architectures for Vision-and-Language Modeling: An Empirical Study
Yixin Nie, Linjie Li, Zhe Gan +6
We initiate the first empirical study on the use of MLP architectures for vision-and-language (VL) fusion. Through extensive experiments on 5 VL tasks and 5 robust VQA benchmarks,…
Human Parity on CommonsenseQA: Augmenting Self-Attention with External Attention
Yichong Xu, Chenguang Zhu, Shuohang Wang +7
Most of today's AI systems focus on using self-attention mechanisms and transformer architectures on large amounts of diverse data to achieve impressive performance gains. In this…
Adversarial GLUE: A Multi-Task Benchmark for Robustness Evaluation of Language Models
Boxin Wang, Chejian Xu, Shuohang Wang +5
Large-scale pre-trained language models have achieved tremendous success across a wide range of natural language understanding (NLU) tasks, even surpassing human performance. Howev…
An Empirical Study of Training End-to-End Vision-and-Language Transformers
Zi-Yi Dou, Yichong Xu, Zhe Gan +9
Vision-and-language (VL) pre-training has proven to be highly effective on various VL downstream tasks. While recent work has shown that fully transformer-based VL models can be mo…
Leveraging Knowledge in Multilingual Commonsense Reasoning
Yuwei Fang, Shuohang Wang, Yichong Xu +4
Commonsense reasoning (CSR) requires the model to be equipped with general world knowledge. While CSR is a language-agnostic process, most comprehensive knowledge sources are in fe…
NOAHQA: Numerical Reasoning with Interpretable Graph Question Answering Dataset
Qiyuan Zhang, Lei Wang, Sicheng Yu +4
While diverse question answering (QA) datasets have been proposed and contributed significantly to the development of deep learning models for QA tasks, the existing datasets fall…
Dict-BERT: Enhancing Language Model Pre-training with Dictionary
Wenhao Yu, Chenguang Zhu, Yuwei Fang +5
Pre-trained language models (PLMs) aim to learn universal language representations by conducting self-supervised training tasks on large-scale corpora. Since PLMs capture word sema…
KG-FiD: Infusing Knowledge Graph in Fusion-in-Decoder for Open-Domain Question Answering
Donghan Yu, Chenguang Zhu, Yuwei Fang +6
Current Open-Domain Question Answering (ODQA) model paradigm often contains a retrieving module and a reading module. Given an input question, the reading module predicts the answe…
Want To Reduce Labeling Cost? GPT-3 Can Help
Shuohang Wang, Yang Liu, Yichong Xu +2
Data annotation is a time-consuming and labor-intensive process for many NLP tasks. Although there exist various methods to produce pseudo data labels, they are often task-specific…
LightningDOT: Pre-training Visual-Semantic Embeddings for Real-Time Image-Text Retrieval
Siqi Sun, Yen-Chun Chen, Linjie Li +3
Multimodal pre-training has propelled great advancement in vision-and-language research. These large-scale pre-trained models, although successful, fatefully suffer from slow infer…
Playing Lottery Tickets with Vision and Language
Zhe Gan, Yen-Chun Chen, Linjie Li +6
Large-scale pre-training has recently revolutionized vision-and-language (VL) research. Models such as LXMERT and UNITER have significantly lifted the state of the art over a wide…
UC2: Universal Cross-lingual Cross-modal Vision-and-Language Pre-training
Mingyang Zhou, Luowei Zhou, Shuohang Wang +4
Vision-and-language pre-training has achieved impressive success in learning multimodal representations between vision and language. To generalize this success to non-English langu…
The Elastic Lottery Ticket Hypothesis
Xiaohan Chen, Yu Cheng, Shuohang Wang +3
Lottery Ticket Hypothesis (LTH) raises keen attention to identifying sparse trainable subnetworks, or winning tickets, which can be trained in isolation to achieve similar or even…
EarlyBERT: Efficient BERT Training via Early-bird Lottery Tickets
Xiaohan Chen, Yu Cheng, Shuohang Wang +3
Heavily overparameterized language models such as BERT, XLNet and T5 have achieved impressive success in many NLP tasks. However, their high model complexity requires enormous comp…
Counterfactual Variable Control for Robust and Interpretable Question Answering
Sicheng Yu, Yulei Niu, Shuohang Wang +2
Deep neural network based question answering (QA) models are neither robust nor explainable in many cases. For example, a multiple-choice QA model, tested without any input of ques…
Cross-Thought for Sentence Encoder Pre-training
Shuohang Wang, Yuwei Fang, Siqi Sun +4
In this paper, we propose Cross-Thought, a novel approach to pre-training sequence encoder, which is instrumental in building reusable sequence embeddings for large-scale NLP tasks…