Extending Audio Context for Long-Form Understanding in Large Audio-Language Models
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 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 (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.
Cite
@article{arxiv.2510.15231,
title = {Extending Audio Context for Long-Form Understanding in Large Audio-Language Models},
author = {Yuatyong Chaichana and Pittawat Taveekitworachai and Warit Sirichotedumrong and Potsawee Manakul and Kunat Pipatanakul},
journal= {arXiv preprint arXiv:2510.15231},
year = {2026}
}
Comments
EACL 2026. Code and dataset are available at: https://github.com/yophis/partial-yarn