@inproceedings{chaichana-etal-2026-extending,
    title = "Extending Audio Context for Long-Form Understanding in Large Audio-Language Models",
    author = "Chaichana, Yuatyong  and
      Taveekitworachai, Pittawat  and
      Sirichotedumrong, Warit  and
      Manakul, Potsawee  and
      Pipatanakul, Kunat",
    editor = "Demberg, Vera  and
      Inui, Kentaro  and
      Marquez, Llu{\'i}s",
    booktitle = "Proceedings of the 19th Conference of the {E}uropean Chapter of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
    month = mar,
    year = "2026",
    address = "Rabat, Morocco",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-eacl/2026.eacl-long.286/",
    pages = "6046--6066",
    ISBN = "979-8-89176-380-7",
    abstract = "Large Audio-Language Models (LALMs) are often constrained by short audio context windows, even when their text backbones support long contexts, limiting long-form audio understanding. Prior work has introduced context-extension methods (e.g. YaRN) on unimodal LLMs, yet their application to LALMs remains unexplored. First, building on RoPE-based context extension, we introduce \textbf{Partial YaRN}, a training-free, modality-decoupled extension method that modifies only audio token positions, leaving text positions intact to preserve the base LLM{'}s text capabilities. Second, we propose Virtual Longform Audio Training (\textbf{VLAT}), a training strategy that extends Partial YaRN into a training-time positional augmentation. VLAT simulates diverse audio lengths during training, enabling generalization to inputs far longer than those seen in training. Our experiments on SALMONN and Qwen2-Audio confirm that Partial YaRN outperforms the original models across wide range of settings, and VLAT provides substantial performance improvement on long audio of unseen lengths."
}