most citedEarly Exiting with Ensemble Internal Classifiers

14 citations · 20 across the 7 of their papers we have counts for

collaborators

7 papers

cs.IR20222 cited

Pre-training for Information Retrieval: Are Hyperlinks Fully Explored?

Jiawen Wu, Xinyu Zhang, Yutao Zhu +7

Recent years have witnessed great progress on applying pre-trained language models, e.g., BERT, to information retrieval (IR) tasks. Hyperlinks, which are commonly used in Web page…

cs.CL2022

Hyperlink-induced Pre-training for Passage Retrieval in Open-domain Question Answering

Jiawei Zhou, Xiaoguang Li, Lifeng Shang +10

To alleviate the data scarcity problem in training question answering systems, recent works propose additional intermediate pre-training for dense passage retrieval (DPR). However,…

cs.CL20221 cited

KMIR: A Benchmark for Evaluating Knowledge Memorization, Identification and Reasoning Abilities of Language Models

Daniel Gao, Yantao Jia, Lei Li +6

Previous works show the great potential of pre-trained language models (PLMs) for storing a large amount of factual knowledge. However, to figure out whether PLMs can be reliable k…

cs.CL20212 cited

Towards More Effective and Economic Sparsely-Activated Model

Hao Jiang, Ke Zhan, Jianwei Qu +14

The sparsely-activated models have achieved great success in natural language processing through large-scale parameters and relatively low computational cost, and gradually become…

cs.IR2021

YES SIR!Optimizing Semantic Space of Negatives with Self-Involvement Ranker

Ruizhi Pu, Xinyu Zhang, Ruofei Lai +7

Pre-trained model such as BERT has been proved to be an effective tool for dealing with Information Retrieval (IR) problems. Due to its inspiring performance, it has been widely us…

cs.IR20211 cited

Pre-training for Ad-hoc Retrieval: Hyperlink is Also You Need

Zhengyi Ma, Zhicheng Dou, Wei Xu +4

Designing pre-training objectives that more closely resemble the downstream tasks for pre-trained language models can lead to better performance at the fine-tuning stage, especiall…