Language is essentially a complex, intricate system of human expressions governed by
grammatical rules. It poses a significant challenge to develop capable AI algorithms for …

Large" instruction-tuned" language models (ie, finetuned to respond to instructions) have
demonstrated a remarkable ability to generalize zero-shot to new tasks. Nevertheless, they …

Large language models (LLMs) have demonstrated impressive zero-shot abilities on a
variety of open-ended tasks, while recent research has also explored the use of LLMs for …

Large language models (LLMs) have revolutionized the field of artificial intelligence,
enabling natural language processing tasks that were previously thought to be exclusive to …

We present Video-LLaMA, a multi-modal framework that empowers Large Language Models
(LLMs) with the capability of understanding both visual and auditory content in the video …

While large language models (LLMs) have demonstrated remarkable capabilities across a
range of downstream tasks, a significant concern revolves around their propensity to exhibit …

Recent AI-assistant agents, such as ChatGPT, predominantly rely on supervised fine-tuning
(SFT) with human annotations and reinforcement learning from human feedback (RLHF) to …

How to efficiently transform large language models (LLMs) into instruction followers is
recently a popular research direction, while training LLM for multi-modal reasoning remains …

In this work we explore recent advances in instruction-tuning language models on a range of
open instruction-following datasets. Despite recent claims that open models can be on par …

In this paper, we uncover a systematic bias in the evaluation paradigm of adopting large
language models~(LLMs), eg, GPT-4, as a referee to score and compare the quality of …