48 citations · 116 across the 8 of their papers we have counts for
11 papers
RankT5: Fine-Tuning T5 for Text Ranking with Ranking Losses
Honglei Zhuang, Zhen Qin, Rolf Jagerman +6
Recently, substantial progress has been made in text ranking based on pretrained language models such as BERT. However, there are limited studies on how to leverage more powerful s…
Knowledge Prompts: Injecting World Knowledge into Language Models through Soft Prompts
Cicero Nogueira dos Santos, Zhe Dong, Daniel Cer +4
Soft prompts have been recently proposed as a tool for adapting large frozen language models (LMs) to new tasks. In this work, we repurpose soft prompts to the task of injecting wo…
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…
Scaling Up Models and Data with and
Adam Roberts, Hyung Won Chung, Anselm Levskaya +40
Recent neural network-based language models have benefited greatly from scaling up the size of training datasets and the number of parameters in the models themselves. Scaling can…
Neural Passage Retrieval with Improved Negative Contrast
Jing Lu, Gustavo Hernandez Abrego, Ji Ma +2
In this paper we explore the effects of negative sampling in dual encoder models used to retrieve passages for automatic question answering. We explore four negative sampling strat…
Learning Visual-Semantic Embeddings for Reporting Abnormal Findings on Chest X-rays
Jianmo Ni, Chun-Nan Hsu, Amilcare Gentili +1
Automatic medical image report generation has drawn growing attention due to its potential to alleviate radiologists' workload. Existing work on report generation often trains enco…