Diffusion Guided Language Modeling
Justin Lovelace, Varsha Kishore, Yiwei Chen, Kilian Q. Weinberger · 2024
Current language models demonstrate remarkable proficiency in text generation.However, for many applications it is desirable to control attributes, such as sentiment, or toxicity, of the generated language-ideally tailored towards each specific use case and target audience.For auto-regressive language models, existing guidance methods are prone to decoding errors that cascade during generation and degrade performance.In contrast, text diffusion models can easily be guided with, for example, a simple linear sentiment classifier-however they do suffer from significantly higher perplexity than auto-regressive alternatives.In this paper we use a guided diffusion model to produce a latent proposal that steers an auto-regressive language model to generate text with desired properties.Our model inherits the unmatched fluency of the auto-regressive approach and the plug-and-play flexibility of diffusion.We show that it outperforms previous plug-andplay guidance methods across a wide range of benchmark data sets.Further, controlling a new attribute in our framework is reduced to training a single logistic regression classifier.Our