GAMA: A Large Audio-Language Model with Advanced Audio Understanding and
  Complex Reasoning Abilities
Authors: name: Sreyan Ghosh, name: Sonal Kumar, name: Ashish Seth, name: Chandra Kiran Reddy Evuru, name: Utkarsh Tyagi, name: S Sakshi, name: Oriol Nieto, name: Ramani Duraiswami, name: Dinesh Manocha

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.