  BatGPT is a large-scale language model designed and trained jointly by Wuhan
University and Shanghai Jiao Tong University. It is capable of generating
highly natural and fluent text in response to various types of input, including
text prompts, images, and audio. In the modeling level, we employ a
bidirectional autoregressive architecture that allows the model to efficiently
capture the complex dependencies of natural language, making it highly
effective in tasks such as language generation, dialog systems, and question
answering. Moreover, the bidirectional autoregressive modeling not only
operates from left to right but also from right to left, effectively reducing
fixed memory effects and alleviating model hallucinations.
  In the training aspect, we propose a novel parameter expansion method for
leveraging the pre-training of smaller models and employ reinforcement learning
from both AI and human feedback, aimed at improving the model's alignment
performance. Overall, these approaches significantly improve the effectiveness
of BatGPT, and the model can be utilized for a wide range of natural language
applications.
