Title: CoFrGeNet: Continued Fraction Architectures for Language Generation
Primary area: foundation or frontier models, including LLMs
Keywords: continued fractions, generative AI, language

Abstract: Transformers are arguably the preferred architecture for language generation. In this paper, inspired by continued fractions, we introduce a new function class for generative modeling. The architecture family implementing this function class is named CoFrGeNets - Continued Fraction Generative Networks. We design novel architectural components based on this function class that can replace Multi-head Attention and Feed-Forward Networks in Transformer blocks while requiring much fewer parameters. We derive custom gradient formulations to optimize the proposed components more accurately and efficiently than using standard PyTorch-based gradients. Our components are a plug-in replacement requiring little change in training or inference procedures that have already been put in place for Transformer-based models thus making our approach easy to incorporate in large industrial workflows. We pre-train our models on two public text datasets - OpenWebText and GneissWeb. Results with our models show that the perplexity and performance on downstream GLUE tasks are superior or competitive with Transformer-based architectures, with two thirds to half the parameters and shorter pre-training time. We believe that future implementations customized to hardware will further bring out the true potential of our architectures.

TL;DR: We propose continued fraction based architectural components to replace attention and ffn in transformers