1 citations · 1 across the 2 of their papers we have counts for
3 papers
cs.CL2023
On Meta-Prompting
Adrian de Wynter, Xun Wang, Qilong Gu +1
Modern large language models (LLMs) are capable of interpreting input strings as instructions, or prompts, and carry out tasks based on them. Unlike traditional learners, LLMs cann…
cs.CL2023★ 1 cited
SCALE: Synergized Collaboration of Asymmetric Language Translation Engines
Xin Cheng, Xun Wang, Tao Ge +4
In this paper, we introduce SCALE, a collaborative framework that connects compact Specialized Translation Models (STMs) and general-purpose Large Language Models (LLMs) as one uni…
cs.CV2023
GLOBER: Coherent Non-autoregressive Video Generation via GLOBal Guided Video DecodER
Mingzhen Sun, Weining Wang, Zihan Qin +3
Video generation necessitates both global coherence and local realism. This work presents a novel non-autoregressive method GLOBER, which first generates global features to obtain…