I'm writing a NeurIPS paper about a new model architecture for processing and generating long texts. Here are some facts about the paper: * The main trick is to replace some of the attention heads with an exponential moving average, where the decay rate is learned for each head. We call this architecture ExeMA. * On language modeling, the perplexity difference between our model and a vanilla transformer is negligible, but that's because next-token prediction is almost always a local task, so perplexity won't be sensitive enough to detect any improvements in long-range understanding. * However, on the SCROLLS benchmark, our model improves by 10% over the baseline. * We also have a new metric for measuring coherence in generated text (CoGnaTe), where our model generates text that is 43% more coherent than the baseline. Help me write the paper's introduction.

Introduction : \n \n N atural language processing ( N LP ) has witness ed significant adv anc ements in recent years , driven by the development of powerful deep learning models such as transform ers . However , these models often struggle with long - range dependencies in text , which can lead to sub opt imal performance in tasks such as language model ing and text generation . In this paper , we introduce a new model architecture called Ex e MA , which addresses this limitation by replacing some of the attention heads with an exponential moving average ( EMA ) mechanism . The decay rate of the E MA is learned for each head , allowing the model to adapt ively attend to different parts of the input sequence . \n \n The main contribution of our work is the introduction of the Ex e MA architecture , which we evaluate on two benchmark s : language model ing and text generation . Our experiments show that the Ex e MA model out per forms the van illa transform er on both tasks , with a 1 0 % improvement in text generation and a 4 3 % increase in coh er ence measure . These results suggest that the Ex e MA architecture is capable of impro ving long - range understanding in N LP tasks , and can be a valuable addition to the tool kit of deep learning models for text processing . \n \n In the following sections , we provide a detailed description of the Ex e MA architecture and its implementation , as well as the experimental setup and results . We also discuss the limitations of our approach and potential directions for future research . </s>