Contributions Our contributions include:
• We propose GPT4Video, a universal framework that aug-
ments LLMs with capabilities for both video understand-
ing and generation.

• We propose a simple and effective fine-tuning method
aimed at improving the safety of video generation, pre-
senting an attractive alternative to the commonly-used
RLHF approach.

• We release our datasets to facilitate future work on multi-
modal LLMs, including (1) a large-scale multimodal in-
struction dataset for video generation that covers a wide
range of conversational scenarios; (2) a comprehensive
multimodal safety benchmark for safety-aware video
generation, with a primary emphasis on explicit content,
since pornography poses the most significant risk in the
realm of video generation.
GPT4Video