GAMA: A Large Audio-Language Model with Advanced Audio Understanding and Complex Reasoning Abilities

TL;DR

GAMA is a large audio-language model that integrates advanced audio understanding with complex reasoning, achieved through novel training methods and datasets, outperforming existing models on diverse audio tasks.

Contribution

The paper introduces GAMA, a new large audio-language model with integrated complex reasoning abilities, developed via innovative instruction tuning and multi-layer audio feature aggregation.

Findings

GAMA outperforms existing models on diverse audio understanding tasks by 1%-84%.

Instruction tuning with CompA-R enhances GAMA's complex reasoning and instruction-following abilities.

GAMA demonstrates superior performance in open-ended audio question-answering.

Abstract

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.