CRoCoDiL: Continuous and Robust Conditioned Diffusion for Language
arXiv:2603.20210v1 Announce Type: new 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. We propose CRoCoDiL (Continuous and Robust Conditioned Diffusion for Language), a unified fine-tuning approach that jointly trains an encoder-demasker architecture, grounding the MDM demasking in continuous latent representations. This leads to the formation of a novel autoencoder in which decoding is obtained by an MDM algorithm. Relying on the same framework, we introduce two unconditional text synthesis algorithms: Continuous-Then-Discrete (ConThenDisc), a hybrid-diffusion approach that first generates latent representations in continuous space and then decodes these to tokens via an MDM, and Contin
arXiv:2603.20210v1 Announce Type: new 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. We propose CRoCoDiL (Continuous and Robust Conditioned Diffusion for Language), a unified fine-tuning approach that jointly trains an encoder-demasker architecture, grounding the MDM demasking in continuous latent representations. This leads to the formation of a novel autoencoder in which decoding is obtained by an MDM algorithm. Relying on the same framework, we introduce two unconditional text synthesis algorithms: Continuous-Then-Discrete (ConThenDisc), a hybrid-diffusion approach that first generates latent representations in continuous space and then decodes these to tokens via an MDM, and Continuous-Within-Discrete (ConWithinDisc), a multi-diffusion strategy that refines latent representations throughout the discrete sampling process. Experiments using LLaDA show that our methods achieve superior generation quality and more than 10x faster sampling speeds in an unconditional setting.
Executive Summary
The article proposes CRoCoDiL, a novel approach to address the limitations of Masked Diffusion Models (MDMs) in language generation. By shifting the diffusion process to a continuous sentence-level semantic space, the authors develop a unified fine-tuning framework that jointly trains an encoder-demasker architecture. This leads to the formation of an autoencoder where decoding is achieved through an MDM algorithm. The authors also introduce two unconditional text synthesis algorithms: ConThenDisc and ConWithinDisc. Experimental results using LLaDA demonstrate superior generation quality and significantly faster sampling speeds. The CRoCoDiL approach has the potential to revolutionize the field of language generation, enabling more efficient and accurate models for various applications.
Key Points
- ▸ CRoCoDiL shifts diffusion process to continuous sentence-level semantic space to address MDM limitations
- ▸ Unified fine-tuning framework jointly trains encoder-demasker architecture
- ▸ ConThenDisc and ConWithinDisc algorithms introduced for unconditional text synthesis
Merits
Strength in Addressing MDM Limitations
CRoCoDiL effectively addresses the token dependencies and semantic incoherence associated with MDMs.
Demerits
High Computational Requirements
The proposed approach may require significant computational resources, limiting its practical applications.
Expert Commentary
The CRoCoDiL approach presents a significant departure from traditional language generation models, leveraging the power of diffusion processes to achieve more accurate and efficient results. While the proposed algorithms demonstrate impressive performance, further research is necessary to fully explore their potential and limitations. Additionally, the computational requirements of the CRoCoDiL approach must be carefully evaluated to ensure its practical applications. As the field of language generation continues to evolve, the CRoCoDiL approach is likely to play a crucial role in shaping the future of language processing.
Recommendations
- ✓ Further research is needed to fully explore the potential and limitations of the CRoCoDiL approach.
- ✓ Careful evaluation of the computational requirements is necessary to ensure the practical applications of CRoCoDiL.
Sources
Original: arXiv - cs.CL
