PLATO-XL: Exploring The Large-scale Pre-training Of Dialogue Generation | Awesome LLM Papers Add your paper to Awesome LLM Papers

PLATO-XL: Exploring The Large-scale Pre-training Of Dialogue Generation

Siqi Bao, Huang He, Fan Wang, Hua Wu, Haifeng Wang, Wenquan Wu, Zhihua Wu, Zhen Guo, Hua Lu, Xinxian Huang, Xin Tian, Xinchao Xu, Yingzhan Lin, Zheng-Yu Niu . Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021 2021 – 65 citations

[Paper]   Search on Google Scholar   Search on Semantic Scholar
ACL Efficiency Image Text Integration Interdisciplinary Approaches Model Architecture Productivity Enhancement Question Answering Training Techniques

To explore the limit of dialogue generation pre-training, we present the models of PLATO-XL with up to 11 billion parameters, trained on both Chinese and English social media conversations. To train such large models, we adopt the architecture of unified transformer with high computation and parameter efficiency. In addition, we carry out multi-party aware pre-training to better distinguish the characteristic information in social media conversations. With such designs, PLATO-XL successfully achieves superior performances as compared to other approaches in both Chinese and English chitchat. We further explore the capacity of PLATO-XL on other conversational tasks, such as knowledge grounded dialogue and task-oriented conversation. The experimental results indicate that PLATO-XL obtains state-of-the-art results across multiple conversational tasks, verifying its potential as a foundation model of conversational AI.

Similar Work