Bridging the Gap Between Training and Inference of Bayesian Controllable Language Models (2206.05519v1)

Abstract: Large-scale pre-trained LLMs have achieved great success on natural language generation tasks. However, it is difficult to control the pre-trained LLMs to generate sentences with the desired attribute such as topic and sentiment, etc. Recently, Bayesian Controllable LLMs (BCLMs) have been shown to be efficient in controllable language generation. Rather than fine-tuning the parameters of pre-trained LLMs, BCLMs use external discriminators to guide the generation of pre-trained LLMs. However, the mismatch between training and inference of BCLMs limits the performance of the models. To address the problem, in this work we propose a "Gemini Discriminator" for controllable language generation which alleviates the mismatch problem with a small computational cost. We tested our method on two controllable language generation tasks: sentiment control and topic control. On both tasks, our method reached achieved new state-of-the-art results in automatic and human evaluations.