2 citations · 6 across the 5 of their papers we have counts for
5 papers
IAG: Induction-Augmented Generation Framework for Answering Reasoning Questions
Zhebin Zhang, Xinyu Zhang, Yuanhang Ren +5
Retrieval-Augmented Generation (RAG), by incorporating external knowledge with parametric memory of language models, has become the state-of-the-art architecture for open-domain QA…
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…
Coarse-to-Fine: Hierarchical Multi-task Learning for Natural Language Understanding
Zhaoye Fei, Yu Tian, Yongkang Wu +9
Generalized text representations are the foundation of many natural language understanding tasks. To fully utilize the different corpus, it is inevitable that models need to unders…
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…
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…