47 citations · 84 across the 7 of their papers we have counts for
7 papers
Open-domain Visual Entity Recognition: Towards Recognizing Millions of Wikipedia Entities
Hexiang Hu, Yi Luan, Yang Chen +5
Large-scale multi-modal pre-training models such as CLIP and PaLI exhibit strong generalization on various visual domains and tasks. However, existing image classification benchmar…
Can Pre-trained Vision and Language Models Answer Visual Information-Seeking Questions?
Yang Chen, Hexiang Hu, Yi Luan +4
Pre-trained vision and language models have demonstrated state-of-the-art capabilities over existing tasks involving images and texts, including visual question answering. However,…
Promptagator: Few-shot Dense Retrieval From 8 Examples
Zhuyun Dai, Vincent Y. Zhao, Ji Ma +7
Much recent research on information retrieval has focused on how to transfer from one task (typically with abundant supervised data) to various other tasks where supervision is lim…
ASQA: Factoid Questions Meet Long-Form Answers
Ivan Stelmakh, Yi Luan, Bhuwan Dhingra +1
An abundance of datasets and availability of reliable evaluation metrics have resulted in strong progress in factoid question answering (QA). This progress, however, does not easil…
Large Dual Encoders Are Generalizable Retrievers
Jianmo Ni, Chen Qu, Jing Lu +8
It has been shown that dual encoders trained on one domain often fail to generalize to other domains for retrieval tasks. One widespread belief is that the bottleneck layer of a du…
CONQRR: Conversational Query Rewriting for Retrieval with Reinforcement Learning
Zeqiu Wu, Yi Luan, Hannah Rashkin +4
Compared to standard retrieval tasks, passage retrieval for conversational question answering (CQA) poses new challenges in understanding the current user question, as each questio…
Contextualized Representations Using Textual Encyclopedic Knowledge
Mandar Joshi, Kenton Lee, Yi Luan +1
We present a method to represent input texts by contextualizing them jointly with dynamically retrieved textual encyclopedic background knowledge from multiple documents. We apply…