OVEL: Large Language Model as Memory Manager for Online Video Entity
  Linking
Haiquan Zhao Xuwu Wang Shisong Chen Zhixu Li Xin Zheng Yanghua Xiao
In recent years, multi-modal entity linking (MEL) has garnered increasing
attention in the research community due to its significance in numerous
multi-modal applications. Video, as a popular means of information
transmission, has become prevalent in people's daily lives. However, most
existing MEL methods primarily focus on linking textual and visual mentions or
offline videos's mentions to entities in multi-modal knowledge bases, with
limited efforts devoted to linking mentions within online video content. In
this paper, we propose a task called Online Video Entity Linking OVEL, aiming
to establish connections between mentions in online videos and a knowledge base
with high accuracy and timeliness. To facilitate the research works of OVEL, we
specifically concentrate on live delivery scenarios and construct a live
delivery entity linking dataset called LIVE. Besides, we propose an evaluation
metric that considers timelessness, robustness, and accuracy. Furthermore, to
effectively handle OVEL task, we leverage a memory block managed by a Large
Language Model and retrieve entity candidates from the knowledge base to
augment LLM performance on memory management. The experimental results prove
the effectiveness and efficiency of our method.