24 citations · 39 across the 7 of their papers we have counts for
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cs.CL2023
FreeLM: Fine-Tuning-Free Language Model
Xiang Li, Xin Jiang, Xuying Meng +2
Pre-trained language models (PLMs) have achieved remarkable success in NLP tasks. Despite the great success, mainstream solutions largely follow the pre-training then finetuning pa…
cs.CL2023★ 7 cited
PanGu-Σ: Towards Trillion Parameter Language Model with Sparse Heterogeneous Computing
Xiaozhe Ren, Pingyi Zhou, Xinfan Meng +14
The scaling of large language models has greatly improved natural language understanding, generation, and reasoning. In this work, we develop a system that trained a trillion-param…
cs.CL2021
LMTurk: Few-Shot Learners as Crowdsourcing Workers in a Language-Model-as-a-Service Framework
Mengjie Zhao, Fei Mi, Yasheng Wang +4
Vast efforts have been devoted to creating high-performance few-shot learners, i.e., large-scale pretrained language models (PLMs) that perform well with little downstream task tra…