Towards Holistic Evaluation of Large Audio-Language Models: A
  Comprehensive Survey
Authors: name: Chih-Kai Yang, name: Neo S. Ho, name: Hung-yi Lee

With advancements in large audio-language models (LALMs), which enhance large
language models (LLMs) with auditory capabilities, these models are expected to
demonstrate universal proficiency across various auditory tasks. While numerous
benchmarks have emerged to assess LALMs' performance, they remain fragmented
and lack a structured taxonomy. To bridge this gap, we conduct a comprehensive
survey and propose a systematic taxonomy for LALM evaluations, categorizing
them into four dimensions based on their objectives: (1) General Auditory
Awareness and Processing, (2) Knowledge and Reasoning, (3) Dialogue-oriented
Ability, and (4) Fairness, Safety, and Trustworthiness. We provide detailed
overviews within each category and highlight challenges in this field, offering
insights into promising future directions. To the best of our knowledge, this
is the first survey specifically focused on the evaluations of LALMs, providing
clear guidelines for the community. We will release the collection of the
surveyed papers and actively maintain it to support ongoing advancements in the
field.