Text Diffusion with Reinforced Conditioning
<|reference_start|>Text Diffusion with Reinforced Conditioning: Diffusion models have demonstrated exceptional capability in generating high-quality images, videos, and audio. Due to their adaptiveness in iterative refinement, they provide a strong potential for achieving better non-autoregressive sequence generation. However, existing text diffusion models still fall short in their performance due to a challenge in handling the discreteness of language. This paper thoroughly analyzes text diffusion models and uncovers two significant limitations: degradation of self-conditioning during training and misalignment between training and sampling. Motivated by our findings, we propose a novel Text Diffusion model called TREC, which mitigates the degradation with Reinforced Conditioning and the misalignment by Time-Aware Variance Scaling. Our extensive experiments demonstrate the competitiveness of TREC against autoregressive, non-autoregressive, and diffusion baselines. Moreover, qualitative analysis shows its advanced ability to fully utilize the diffusion process in refining samples.<|reference_end|>
@article{liu2024text,
    title={Text Diffusion with Reinforced Conditioning},
    author={Yuxuan Liu, Tianchi Yang, Shaohan Huang, Zihan Zhang, Haizhen Huang,
  Furu Wei, Weiwei Deng, Feng Sun, Qi Zhang},
    journal={arXiv preprint arXiv:2402.14843},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.14843},
    primaryClass={cs.CL cs.AI cs.LG}
}