Large Language Models (LLMs) have recently demonstrated remarkable capabilities in
natural language processing tasks and beyond. This success of LLMs has led to a large …

Efficient fine-tuning is vital for adapting large language models (LLMs) to downstream tasks.
However, it requires non-trivial efforts to implement these methods on different models. We …

Video captioning (VC) is a fast-moving, cross-disciplinary area of research that comprises
contributions from domains such as computer vision, natural language processing …

Pre-trained models have achieved state-of-the-art results in various Natural Language
Processing (NLP) tasks. Recent works such as T5 and GPT-3 have shown that scaling up …

Pretrained language models (LMs) are susceptible to generate text with nonfactual
information. In this work, we measure and improve the factual accuracy of large-scale LMs …

In this paper, we take the advantage of previous pre-trained models (PTMs) and propose a
novel Chinese pre-trained unbalanced transformer (CPT). Different from previous Chinese …

This work aims at decreasing the end-to-end generation latency of large language models
(LLMs). One of the major causes of the high generation latency is the sequential decoding …

Story generation, namely, generating a reasonable story from a leading context, is an
important but challenging task. In spite of the success in modeling fluency and local …

Large Language Models (LLMs), such as ChatGPT, LLaMA, GLM, and PaLM, have
exhibited remarkable performances across various tasks in recent years. However, LLMs …

Existing pre-trained large language models have shown unparalleled generative
capabilities. However, they are not controllable. In this paper, we propose MEGATRON …