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Our winning entry for the CVPR 2023 Generic Event Boundary Captioning (GEBC)competition is detailed in this paper. Unlike conventional video captioningtasks, GEBC demands that the captioning model possess an understanding ofimmediate changes in status around the designated video boundary, making it adifficult task. This paper proposes an effective model LLMVA-GEBC (LargeLanguage Model with Video Adapter for Generic Event Boundary Captioning): (1)We utilize a pretrained LLM for generating human-like captions with highquality. (2) To adapt the model to the GEBC task, we take the video Q-former asan adapter and train it with the frozen visual feature extractors and LLM. Ourproposed method achieved a 76.14 score on the test set and won the first placein the challenge. Our code is available athttps://github.com/zjr2000/LLMVA-GEBC .

LLMVA-GEBC: Large Language Model with Video Adapter for Generic Event Boundary Captioning