Discrete Token Models
Discrete Token Models refer to architectures in natural language processing that generate or process data as a sequence of discrete symbols (tokens) from a finite vocabulary. This category primarily encompasses Auto-Regressive Modeling and emerging non-autoregressive approaches like diffusion-based text generation.
Core Architectures
- Auto-Regressive Models: The dominant paradigm where tokens are generated sequentially, conditioning each step on previous outputs. Includes Transformers and LLMs.
- Non-Autoregressive / Parallel Generation: Emerging methods aiming to reduce inference latency by generating tokens in parallel or via iterative refinement rather than strict left-to-right dependency.
Recent Developments & Innovations
- Text Diffusion Adaptation: Google DeepMind has explored adapting diffusion processes, traditionally used for continuous image data, to discrete text generation.
- Text Diffusion: Google DeepMind’s Faster Parallel Text Generation via Denoising details how this approach utilizes denoising mechanisms to accelerate parallel text generation, challenging the sequential bottleneck of standard autoregressive models.
- Discrete Latent Spaces: Techniques mapping continuous latent variables to discrete codebooks (e.g., VQ-VAE) to bridge generative image and text domains.
Key Challenges
- Granularity & Lossiness: Discretization inevitably loses information compared to continuous representations.
- Inference Speed: While parallel methods like Text Diffusion show promise, achieving stability and coherence comparable to strong autoregressive baselines remains a research focus.
- Vocabulary Size Scaling: Managing efficiency as token sets grow larger in multimodal contexts.