Diffusion Guided Language Modeling

Justin Lovelace, Varsha Kishore, Yiwei Chen, Kilian Weinberger

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.
Anthology ID:
2024.findings-acl.887
Volume:
Findings of the Association for Computational Linguistics: ACL 2024
Month:
August
Year:
2024
Address:
Bangkok, Thailand
Editors:
Lun-Wei Ku, Andre Martins, Vivek Srikumar
Venue:
Findings
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
14936–14952
Language:
URL:
https://preview.aclanthology.org/build-pipeline-with-new-library/2024.findings-acl.887/
DOI:
10.18653/v1/2024.findings-acl.887
Bibkey:
Cite (ACL):
Justin Lovelace, Varsha Kishore, Yiwei Chen, and Kilian Weinberger. 2024. Diffusion Guided Language Modeling. In Findings of the Association for Computational Linguistics: ACL 2024, pages 14936–14952, Bangkok, Thailand. Association for Computational Linguistics.
Cite (Informal):
Diffusion Guided Language Modeling (Lovelace et al., Findings 2024)
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PDF:
https://preview.aclanthology.org/build-pipeline-with-new-library/2024.findings-acl.887.pdf
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