GAMA: A Large Audio-Language Model with Advanced Audio Understanding and Complex Reasoning Abilities
<|reference_start|>GAMA: A Large Audio-Language Model with Advanced Audio Understanding and Complex Reasoning Abilities: Perceiving and understanding non-speech sounds and non-verbal speech is essential to making decisions that help us interact with our surroundings. In this paper, we propose GAMA, a novel General-purpose Large Audio-Language Model (LALM) with Advanced Audio Understanding and Complex Reasoning Abilities. We build GAMA by integrating an LLM with multiple types of audio representations, including features from a custom Audio Q-Former, a multi-layer aggregator that aggregates features from multiple layers of an audio encoder. We fine-tune GAMA on a large-scale audio-language dataset, which augments it with audio understanding capabilities. Next, we propose CompA-R (Instruction-Tuning for Complex Audio Reasoning), a synthetically generated instruction-tuning (IT) dataset with instructions that require the model to perform complex reasoning on the input audio. We instruction-tune GAMA with CompA-R to endow it with complex reasoning abilities, where we further add a soft prompt as input with high-level semantic evidence by leveraging event tags of the input audio. Finally, we also propose CompA-R-test, a human-labeled evaluation dataset for evaluating the capabilities of LALMs on open-ended audio question-answering that requires complex reasoning. Through automated and expert human evaluations, we show that GAMA outperforms all other LALMs in literature on diverse audio understanding tasks by margins of 1%-84%. Further, GAMA IT-ed on CompA-R proves to be superior in its complex reasoning and instruction following capabilities.<|reference_end|>
@article{ghosh2024gama:,
    title={GAMA: A Large Audio-Language Model with Advanced Audio Understanding and
  Complex Reasoning Abilities},
    author={Sreyan Ghosh and Sonal Kumar and Ashish Seth and Chandra Kiran Reddy
  Evuru and Utkarsh Tyagi and S Sakshi and Oriol Nieto and Ramani Duraiswami
  and Dinesh Manocha},
    journal={arXiv preprint arXiv:2406.11768},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.11768},
    primaryClass={cs.SD cs.AI cs.CL eess.AS}
}