Diffusion-LM Improves Controllable Text Generation

• URL: http://arxiv.org/abs/2205.14217v1
• Date: Fri, 27 May 2022 20:12:09 GMT
• Title: Diffusion-LM Improves Controllable Text Generation
• Authors: Xiang Lisa Li, John Thickstun, Ishaan Gulrajani, Percy Liang, Tatsunori B. Hashimoto
• Abstract summary: Controlling the behavior of language models (LMs) without re-training is a major open problem in natural language generation. We develop a new non-autoregressive language model based on continuous diffusions that we call Diffusion-LM. We demonstrate successful control of Diffusion-LM for six challenging fine-grained control tasks, significantly outperforming prior work.
• Score: 80.50044830018442
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Controlling the behavior of language models (LMs) without re-training is a major open problem in natural language generation. While recent works have demonstrated successes on controlling simple sentence attributes (e.g., sentiment), there has been little progress on complex, fine-grained controls (e.g., syntactic structure). To address this challenge, we develop a new non-autoregressive language model based on continuous diffusions that we call Diffusion-LM. Building upon the recent successes of diffusion models in continuous domains, Diffusion-LM iteratively denoises a sequence of Gaussian vectors into word vectors, yielding a sequence of intermediate latent variables. The continuous, hierarchical nature of these intermediate variables enables a simple gradient-based algorithm to perform complex, controllable generation tasks. We demonstrate successful control of Diffusion-LM for six challenging fine-grained control tasks, significantly outperforming prior work.

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