Year: 2025
Type: conference-paper
Abstract: 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.
Authors: Chih-Kai Yang, Neo S. Ho, Hung-yi Lee
Institution: National Taiwan University
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
Funder: Ministry of Education, India
OpenAlex ID: W4416036756