4 articles tagged with #non-autoregressive. AI-curated summaries with sentiment analysis and key takeaways from 50+ sources.
Researchers propose Cola DLM, a hierarchical latent diffusion language model that generates text through continuous semantic modeling rather than traditional left-to-right autoregressive decoding. The approach achieves comparable performance to autoregressive models while offering greater flexibility, better scaling properties, and a potential pathway for unified modeling across discrete and continuous modalities.
Researchers identify a critical failure mode in non-autoregressive diffusion language models caused by proximity bias, where the denoising process concentrates on adjacent tokens, creating spatial error propagation. They propose a minimal-intervention approach using a lightweight planner and temperature annealing to guide early token selection, achieving substantial improvements on reasoning and planning tasks.
Researchers introduce SyncSpeech, a new text-to-speech model that combines autoregressive and non-autoregressive approaches using a Temporal Mask Transformer architecture. The model achieves 5.8x lower first-packet latency and 8.8x improved real-time performance while maintaining comparable speech quality to existing models.
Researchers identify why Diffusion Language Models (DLMs) struggle with parallel token generation, finding that training data structure forces autoregressive-like behavior. They propose NAP, a data-centric approach using multiple independent reasoning trajectories that improves parallel decoding performance on math benchmarks.
