How Auditory Knowledge in LLM Backbones Shapes Audio Language Models: A Holistic Evaluation
Large language models (LLMs) have been widely used as knowledge backbones of Large Audio Language Models (LALMs), yet how much auditory knowledge they encode...
2026 IEEE Spoken Language Technology Workshop (SLT) , 2026
Abstract
Large language models (LLMs) have been widely used as knowledge backbones of Large Audio Language Models (LALMs), yet how much auditory knowledge they encode through text-only pre-training and how this affects downstream performance remains unclear. We study this gap by comparing different LLMs under two text-only and one audio-grounded setting: (1) direct probing on AKB-2000, a curated benchmark testing the breadth and depth of auditory knowledge; (2) cascade evaluation, where LLMs reason over text descriptions from an audio captioner; and (3) audio-grounded evaluation, where each LLM is fine-tuned into a Large Audio Language Model (LALM) with an audio encoder. Our findings reveal that auditory knowledge varies substantially across families, and text-only results are strongly correlated with audio performance. Our work provides empirical grounding for a comprehensive understanding of LLMs in audio research.
BibTeX
@inproceedings{lu2026auditory,
title = {How Auditory Knowledge in LLM Backbones Shapes Audio Language Models: A Holistic Evaluation},
author = {Lu, Ke-Han and Fu, Szu-Wei and Yang, Chao-Han Huck and Chen, Zhehuai and Huang, Sung-Feng and Yang, Chih-Kai and Lin, Yi-Cheng and Hsiao, Chi-Yuan and Ren, Wenze and Hu, En-Pei and Huang, Yu-Han and Cheng, An-Yu and Chiang, Cheng-Han and Tsao, Yu and Wang, Yu-Chiang Frank and Lee, Hung-yi},
booktitle = {2026 IEEE Spoken Language Technology Workshop (SLT)},
year = {2026},
}