Quantized Embedding Vectors for Controllable Diffusion Language Models

arXiv:2402.10107v1 Announce Type: new
Abstract: Improving the controllability, portability, and inference speed of diffusion language models (DLMs) is a key challenge in natural language generation. While recent research has shown significant success in complex text generation with language models, the memory and computational power are still very demanding and fall short of expectations, which naturally results in low portability and instability for the models. To mitigate these issues, numerous well-established methods were proposed for neural network quantization. To further enhance …

abstract arxiv challenge computational cs.ai cs.cl diffusion embedding inference key language language generation language models memory natural natural language natural language generation portability power research speed success text text generation type vectors