  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 their
portability of independent deployment as well as improve their stability
evaluated by language perplexity, we propose a novel approach called the
Quantized Embedding Controllable Diffusion Language Model (QE-CDLM). QE-CDLM
builds upon the recent successful controllable DLMs by remodeling the
task-specific embedding space via quantization. This leads to a gradient-based
controller for the generation tasks, and more stable intermediate latent
variables are obtained, which naturally brings in an accelerated convergence as
well as better controllability. Additionally, the adaption fine-tuning method
is employed to reduce tunable weights. Experimental results on five challenging
fine-grained control tasks demonstrate that QE-CDLM compares favorably to
existing methods in terms of quality and feasibility, achieving better
perplexity and lightweight fine-tuning.
