3 accepted papers
A central challenge in large language model inference is the trade-off between generation speed and output quality. Autoregressive models produce high-quality text but generate tokens sequentially. Diffusion models can generate tokens in parallel but often need many iterations to match the same qual…
The rise of large language models (LLMs) has introduced a new type of programs: Natural language programs. By writing prompts directing LLMs to perform natural language processing, code generation, reasoning, etc., LLM users are writing code in natural language for the LLM to execute. An emerging t…
Decoding with autoregressive language models traditionally occurs sequentially, generating one token after another. Recent attempts to introduce parallelism require a pre-determined structure in the generated content to implement parallel generation, such as by pattern-matching on bullet points. In…