@inproceedings{kumar-2025-language,
    title = "Language-Guided Temporal Token Pruning for Efficient {V}ideo{LLM} Processing",
    author = "Kumar, Yogesh",
    editor = "Christodoulopoulos, Christos  and
      Chakraborty, Tanmoy  and
      Rose, Carolyn  and
      Peng, Violet",
    booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/author-page-yu-wang-polytechnic/2025.emnlp-main.451/",
    doi = "10.18653/v1/2025.emnlp-main.451",
    pages = "8935--8942",
    ISBN = "979-8-89176-332-6",
    abstract = "Vision Language Models (VLMs) struggle with long-form videos due to the quadratic complexity of attention mechanisms. We propose Language-Guided Temporal Token Pruning (LGTTP), which leverages temporal cues from queries to adaptively prune video tokens, preserving contextual continuity while reducing computational overhead. Unlike uniform pruning or keyframe selection, LGTTP retains higher token density in temporally relevant segments. Our model-agnostic framework integrates with TimeChat and LLaVA-Video, achieving a 65{\%} reduction in computation while preserving 97-99{\%} of the original performance. On QVHighlights, LGTTP improves HIT@1 by +9.5{\%}, and on Charades-STA, it retains 99.6{\%} of R@1. It excels on queries with explicit temporal markers and remains effective across general video understanding tasks."
}