CRoCoDiL: Continuous and Robust Conditioned Diffusion for Language
Summary: arXiv:2603.20210v3 Announce Type: replace-cross
Abstract: Masked Diffusion Models (MDMs) provide an efficient non-causal alternative to autoregressive generation but often struggle with token dependencies and semantic incoherence due to their reliance on discrete marginal distributions. We address these limitations by shifting the diffusion process into a continuous sentence-level semantic space.
Introduction
Recent advancements in natural language processing have highlighted the limitations of traditional autoregressive models, particularly in terms of token dependencies and semantic coherence. Masked Diffusion Models (MDMs) have emerged as a viable alternative, offering a non-causal approach to text generation. However, MDMs often encounter issues due to their reliance on discrete marginal distributions, leading to challenges in maintaining context and coherence in generated text. To overcome these challenges, a new approach known as CRoCoDiL (Continuous and Robust Conditioned Diffusion for Language) has been proposed.
Key Features of CRoCoDiL
CRoCoDiL introduces a unified fine-tuning approach that enhances the capabilities of MDMs through the following innovations:
- Continuous Sentence-Level Semantic Space: The diffusion process is transitioned into a continuous semantic space, allowing for more coherent and contextually relevant text generation.
- Encoder-Demasker Architecture: This architecture jointly trains the demasking process within a continuous latent representation framework, resulting in improved semantic coherence.
- Novel Autoencoder Design: The model operates as a novel autoencoder where decoding is facilitated through an MDM algorithm, enhancing the quality of generated outputs.
Unconditional Text Synthesis Algorithms
In addition to the improvements in MDMs, CRoCoDiL introduces two innovative unconditional text synthesis algorithms:
- Continuous-Then-Discrete (ConThenDisc): This hybrid-diffusion method first generates latent representations in a continuous space and subsequently decodes these representations into tokens using an MDM.
- Continuous-Within-Discrete (ConWithinDisc): This multi-diffusion strategy refines latent representations throughout the discrete sampling process, enhancing the overall quality of the generated text.
Experimental Results
Comprehensive experiments conducted using the LLaDA framework demonstrate that the CRoCoDiL methods achieve remarkable performance improvements:
- Superior generation quality compared to traditional MDMs.
- More than 10 times faster sampling speeds in an unconditional setting, making the generation process more efficient and practical for real-world applications.
Conclusion
CRoCoDiL represents a significant advancement in the field of language generation, addressing the key shortcomings of existing masked diffusion models. By integrating continuous semantic representations and introducing novel synthesis algorithms, CRoCoDiL not only enhances the quality of generated text but also optimizes the efficiency of the generation process. As the field continues to evolve, CRoCoDiL stands as a promising approach for future research and application in natural language processing.