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20222025
most citedFengshenbang 1.0: Being the Foundation of Chinese Cognitive Intelligence

47 citations · 74 across the 15 of their papers we have counts for

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Showing 2022Show all

6 papers · 1 filter

cs.IR2022

Corrected Evaluation Results of the NTCIR WWW-2, WWW-3, and WWW-4 English Subtasks

Tetsuya Sakai, Sijie Tao, Maria Maistro +7

Unfortunately, the official English (sub)task results reported in the NTCIR-14 WWW-2, NTCIR-15 WWW-3, and NTCIR-16 WWW-4 overview papers are incorrect due to noise in the official…

cs.CL2022★ 1 cited

Zero-Shot Learners for Natural Language Understanding via a Unified Multiple Choice Perspective

Ping Yang, Junjie Wang, Ruyi Gan +6

We propose a new paradigm for zero-shot learners that is format agnostic, i.e., it is compatible with any format and applicable to a list of language tasks, such as text classifica…

cs.CL2022★ 16 cited

Solving Math Word Problems via Cooperative Reasoning induced Language Models

Xinyu Zhu, Junjie Wang, Lin Zhang +4

Large-scale pre-trained language models (PLMs) bring new opportunities to challenging problems, especially those that need high-level intelligence, such as the math word problem (M…

cs.CV2022★ 4 cited

MAP: Multimodal Uncertainty-Aware Vision-Language Pre-training Model

Yatai Ji, Junjie Wang, Yuan Gong +6

Multimodal semantic understanding often has to deal with uncertainty, which means the obtained messages tend to refer to multiple targets. Such uncertainty is problematic for our i…

cs.CL2022★ 47 cited

Fengshenbang 1.0: Being the Foundation of Chinese Cognitive Intelligence

Jiaxing Zhang, Ruyi Gan, Junjie Wang +22

Nowadays, foundation models become one of fundamental infrastructures in artificial intelligence, paving ways to the general intelligence. However, the reality presents two urgent…

cs.CL2022

Towards No.1 in CLUE Semantic Matching Challenge: Pre-trained Language Model Erlangshen with Propensity-Corrected Loss

Junjie Wang, Yuxiang Zhang, Ping Yang +1

This report describes a pre-trained language model Erlangshen with propensity-corrected loss, the No.1 in CLUE Semantic Matching Challenge. In the pre-training stage, we construct…