52 citations · 139 across the 17 of their papers we have counts for
8 papers · 1 filter
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…
Unifying Vision, Text, and Layout for Universal Document Processing
Zineng Tang, Ziyi Yang, Guoxin Wang +6
We propose Universal Document Processing (UDOP), a foundation Document AI model which unifies text, image, and layout modalities together with varied task formats, including docume…
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…
UniSumm and SummZoo: Unified Model and Diverse Benchmark for Few-Shot Summarization
Yulong Chen, Yang Liu, Ruochen Xu +4
The high annotation costs and diverse demands of various summarization tasks motivate the development of few-shot summarization. However, despite the emergence of many summarizatio…
MACSum: Controllable Summarization with Mixed Attributes
Yusen Zhang, Yang Liu, Ziyi Yang +6
Controllable summarization allows users to generate customized summaries with specified attributes. However, due to the lack of designated annotations of controlled summaries, exis…
Global Contrastive Batch Sampling via Optimization on Sample Permutations
Vin Sachidananda, Ziyi Yang, Chenguang Zhu
Contrastive Learning has recently achieved state-of-the-art performance in a wide range of tasks. Many contrastive learning approaches use mined hard negatives to make batches more…
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…
Automatic Rule Induction for Interpretable Semi-Supervised Learning
Reid Pryzant, Ziyi Yang, Yichong Xu +2
Semi-supervised learning has shown promise in allowing NLP models to generalize from small amounts of labeled data. Meanwhile, pretrained transformer models act as black-box correl…