Diffusion Guided Language Modeling

• URL: http://arxiv.org/abs/2408.04220v1
• Date: Thu, 8 Aug 2024 05:06:22 GMT
• Title: Diffusion Guided Language Modeling
• Authors: Justin Lovelace, Varsha Kishore, Yiwei Chen, Kilian Q. Weinberger,
• Abstract summary: For many applications it is desirable to control attributes, such as sentiment, of the generated language. For auto-regressive language models, existing guidance methods are prone to decoding errors that cascade during generation and degrade performance. 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.
• Score: 28.819061884362792
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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-and-play 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.

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