5 papers
Tri-PvP: Exposing Modality Bias in Omni-Modal Large Language Models through Perceptual-Propositional Evidence Conflicts
Yen-Ting Piao, Shu-Yun Chen, Chin-Hui Chu +4
Omni-modal large language models (OLLMs) jointly process vision, audio, and text, yet their modality bias under cross-modal conflict remains underexplored. Existing benchmarks conf…
When Demonstrations Fail: Diagnosing the Limits of In-Context Learning in Large Audio-Language Models with Progressive Cue Removal
Yen-Ting Piao, Jay Chiehen Liao, Wei-Tang Chien +5
While Large Audio-Language Models (LALMs) have been shown to exhibit degraded instruction-following capabilities, their ability to infer task patterns from in-context examples with…
MUGEN: Evaluating and Improving Multi-audio Understanding of Large Audio-Language Models
Chih-Kai Yang, Yun-Shao Tsai, Yu-Kai Guo +7
While multi-audio understanding is critical for large audio-language models (LALMs), it remains underexplored. We introduce MUGEN, a comprehensive benchmark evaluating this capabil…
SAKE: Towards Editing Auditory Attribute Knowledge of Large Audio-Language Models
Chih-Kai Yang, Yen-Ting Piao, Tzu-Wen Hsu +8
Knowledge editing enables targeted updates without retraining, but prior work focuses on textual or visual facts, leaving abstract auditory perceptual knowledge underexplored. We i…
SAKURA: On the Multi-hop Reasoning of Large Audio-Language Models Based on Speech and Audio Information
Chih-Kai Yang, Neo Ho, Yen-Ting Piao +1
Large audio-language models (LALMs) extend the large language models with multimodal understanding in speech, audio, etc. While their performances on speech and audio-processing ta…