Apple research: Diffusion-based model speeds up long-form text generation
Apple researchers explore diffusion-based models to speed up long-form text generation Apple researchers have introduced a new language model that uses a diffusion-based approach to generate text. According to the initial report, this model can produce longer passages significantly faster than conventional autoregressive models (the token-by-token approach used by systems like ChatGPT). Key points: Diffusion-based technique: Unlike autoregressive models that generate tokens in a fixed sequence, diffusion methods iteratively refine text and may allow faster handling of long sequences. Better performance for long-form content: The new model reportedly excels when producing extended passages, reducing generation time compared to typical autoregressive approaches. Research implications: If diffusion approaches scale well, they could change how large language models are built for tasks like long-form writing, summarization, and document synthesis. The announcement (reported by…