  Video Temporal Grounding (VTG) focuses on accurately identifying event
timestamps within a particular video based on a linguistic query, playing a
vital role in downstream tasks such as video browsing and editing. While Video
Large Language Models (video LLMs) have made significant progress in
understanding video content, they often face challenges in accurately
pinpointing timestamps within videos, which limits their performance on VTG
tasks. Therefore, to improve video LLMs' ability to effectively locate
timestamps, we argue that two critical aspects need to be enhanced. First, it
is essential to have high-quality instructional tuning datasets that encompass
mainstream VTG tasks. Second, directly incorporating timestamp knowledge into
video LLMs is crucial, as it enables models to efficiently comprehend timestamp
information. To address these needs, we first introduce VTG-IT-120K, a
high-quality and comprehensive instruction tuning dataset that covers VTG tasks
such as moment retrieval, dense video captioning, video summarization, and
video highlight detection. Furthermore, we propose a specially designed video
LLM model for VTG tasks, VTG-LLM, which (1) effectively integrates timestamp
knowledge into visual tokens; (2) incorporates absolute-time tokens that
specifically handle timestamp knowledge, thereby avoiding concept shifts; and
(3) introduces a lightweight, high-performance slot-based token compression
method to facilitate the sampling of more video frames. Comprehensive
experiments showcase the superior performance of VTG-LLM in comparison to other
video LLM methods across various VTG tasks. Our code and datasets are available
at \url{https://github.com/gyxxyg/VTG-LLM}.
