Large models represent a groundbreaking advancement in multiple application fields,
enabling remarkable achievements across various tasks. However, their unprecedented …

Large language models (LLMs) have significantly advanced the field of natural language
processing (NLP), providing a highly useful, task-agnostic foundation for a wide range of …

Reinforcement Learning from Human Feedback (RLHF) facilitates the alignment of large
language models with human preferences, significantly enhancing the quality of interactions …

Abstract Reinforcement Learning from Human Feedback (RLHF) facilitates the alignment of
large language models with human preferences, significantly enhancing the quality of …

Fine-tuning pre-trained models has been ubiquitously proven to be effective in a wide range
of NLP tasks. However, fine-tuning the whole model is parameter inefficient as it always …

Objective The primary aim of this research was to address the limitations observed in the
medical knowledge of prevalent large language models (LLMs) such as ChatGPT, by …

Diffusion models have proven to be highly effective in generating high-quality images.
However, adapting large pre-trained diffusion models to new domains remains an open …

With the continuous growth in the number of parameters of transformer-based pretrained
language models (PLMs), particularly the emergence of large language models (LLMs) with …

Large language models (LLMs) with enormous pre-training tokens and parameter amounts
emerge abilities, including math reasoning, code generation, and instruction following …