Inference with modern Large Language Models (LLMs) is expensive and time-consuming,
and speculative sampling has proven to be an effective solution. Most speculative sampling …

Since the onset of LLMs, translating natural language queries to structured SQL commands
is assuming increasing. Unlike the previous reviews, this survey provides a comprehensive …

The current large auto-regressive models can generate high-quality, high-resolution images,
but these models require hundreds or even thousands of steps of next-token prediction …

Generating accurate SQL according to natural language questions (text-to-SQL) is a long-
standing problem since it is challenging in user question understanding, database schema …

In this paper, we propose ZipAR, a training-free, plug-and-play parallel decoding framework
for accelerating auto-regressive (AR) visual generation. The motivation stems from the …

The rapid growth in the parameters of large language models (LLMs) has made inference
latency a fundamental bottleneck, limiting broader application of LLMs. Speculative …

In the endeavor to make autonomous robots take actions, task planning is a major challenge
that requires translating high-level task descriptions into long-horizon action sequences …

A major concern with integrating large language model (LLM) services (eg, ChatGPT) into
workplaces is that employees may inadvertently leak sensitive information through their …

Scaling up language models has significantly increased their capabilities. But larger models
are slower models, and so there is now an extensive body of work (eg, speculative sampling …

Autoregressive models have emerged as a powerful approach for visual generation but
suffer from slow inference speed due to their sequential token-by-token prediction process …