Diffusion Guided Language Modeling
<|reference_start|>Diffusion Guided Language Modeling: 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.<|reference_end|>
@article{lovelace2024diffusion,
    title={Diffusion Guided Language Modeling},
    author={Justin Lovelace, Varsha Kishore, Yiwei Chen, Kilian Q. Weinberger},
    journal={arXiv preprint arXiv:2408.04220},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.04220},
    primaryClass={cs.CL cs.LG}
}