2 citations · 6 across the 5 of their papers we have counts for
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cs.CL2023
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…
cs.CL2022★ 2 cited
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…
cs.CL2021★ 2 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…