479 citations · 1.9k across the 94 of their papers we have counts for
11 papers · 1 filter
CValues: Measuring the Values of Chinese Large Language Models from Safety to Responsibility
Guohai Xu, Jiayi Liu, Ming Yan +11
With the rapid evolution of large language models (LLMs), there is a growing concern that they may pose risks or have negative social impacts. Therefore, evaluation of human values…
ChatPLUG: Open-Domain Generative Dialogue System with Internet-Augmented Instruction Tuning for Digital Human
Junfeng Tian, Hehong Chen, Guohai Xu +14
In this paper, we present ChatPLUG, a Chinese open-domain dialogue system for digital human applications that instruction finetunes on a wide range of dialogue tasks in a unified i…
Knowledge Distillation of Transformer-based Language Models Revisited
Chengqiang Lu, Jianwei Zhang, Yunfei Chu +5
In the past few years, transformer-based pre-trained language models have achieved astounding success in both industry and academia. However, the large model size and high run-time…
mPLUG: Effective and Efficient Vision-Language Learning by Cross-modal Skip-connections
Chenliang Li, Haiyang Xu, Junfeng Tian +12
Large-scale pretrained foundation models have been an emerging paradigm for building artificial intelligence (AI) systems, which can be quickly adapted to a wide range of downstrea…
Learning Relation Alignment for Calibrated Cross-modal Retrieval
Shuhuai Ren, Junyang Lin, Guangxiang Zhao +5
Despite the achievements of large-scale multimodal pre-training approaches, cross-modal retrieval, e.g., image-text retrieval, remains a challenging task. To bridge the semantic ga…
Sketch and Refine: Towards Faithful and Informative Table-to-Text Generation
Peng Wang, Junyang Lin, An Yang +4
Table-to-text generation refers to generating a descriptive text from a key-value table. Traditional autoregressive methods, though can generate text with high fluency, suffer from…
M6: A Chinese Multimodal Pretrainer
Junyang Lin, Rui Men, An Yang +22
In this work, we construct the largest dataset for multimodal pretraining in Chinese, which consists of over 1.9TB images and 292GB texts that cover a wide range of domains. We pro…
Taming the Expressiveness and Programmability of Graph Analytical Queries
Lu Qin, Longbin Lai, Kongzhang Hao +6
Graph database has enjoyed a boom in the last decade, and graph queries accordingly gain a lot of attentions from both the academia and industry. We focus on analytical queries in…
InterBERT: Vision-and-Language Interaction for Multi-modal Pretraining
Junyang Lin, An Yang, Yichang Zhang +3
Multi-modal pretraining for learning high-level multi-modal representation is a further step towards deep learning and artificial intelligence. In this work, we propose a novel mod…
AdaBERT: Task-Adaptive BERT Compression with Differentiable Neural Architecture Search
Daoyuan Chen, Yaliang Li, Minghui Qiu +7
Large pre-trained language models such as BERT have shown their effectiveness in various natural language processing tasks. However, the huge parameter size makes them difficult to…
Towards Knowledge-Based Personalized Product Description Generation in E-commerce
Qibin Chen, Junyang Lin, Yichang Zhang +3
Quality product descriptions are critical for providing competitive customer experience in an e-commerce platform. An accurate and attractive description not only helps customers m…