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.

Of course , I ' d be happy to help you write the introduction for your Ne ur IP S paper ! Here ' s a possible draft : \n Introduction : \n In recent years , transform er - based language models have achieved state - of - the - art results in a wide range of natural language processing ( N LP ) tasks . However , these models are not without their limitations . One of the main challeng es is their in ability to effectively capture long - range dependencies in text , leading to reduced coh er ence and read ability in generated text . \n To address this challenge , we propose a novel architecture called Ex e MA , which rep laces some of the attention heads in a transform er model with an exponential moving average ( EMA ). By learning the decay rate for each head , Ex e MA is able to adapt ively weight the importance of different parts of the input sequence , allowing it to capture both local and long - range dependencies . \n We evaluate Ex e MA on two benchmark s : language model ing and text generation . Our results show that Ex e MA ach ieves neg lig ible per plex ity differences compared to a van illa transform er on language model ing tasks , but demonstr ates significant improvements on the SC RO LL S benchmark , impro ving by 1 0 % over the bas eline . Additionally , we introduce a new metric for meas uring coh er ence in generated text , called Co G na Te , which shows that Ex e MA generates text that is 4 3 % more coh er ent than the bas eline . \n Over all , our experiments demonstrate the effect iveness of Ex e MA in impro ving the performance of transform er - based language models on long - range dependencies , and its potential to generate more coh er ent and readable text . In the following sections , we provide a more detailed explanation of our method and results , as well as discuss the imp lications of our find ings for future research in N LP . </s>