5 papers · 1 filter
INSPIRE: A Benchmark for Instruction-Aware Speech Retrieval
Chen-An Li, Hung-yi Lee
Existing speech retrieval systems rely on fixed similarity matching and cannot adapt to diverse user intents. We introduce INSPIRE, the first benchmark for instruction-aware speech…
All That Glitters Is Not Audio: Rethinking Text Priors and Audio Reliance in Audio-Language Evaluation
Leonardo Haw-Yang Foo, Chih-Kai Yang, Chen-An Li +2
Large Audio-Language Models show consistent performance gains across speech and audio benchmarks, yet high scores may not reflect true auditory perception. If a model can answer qu…
Causal Tracing of Audio-Text Fusion in Large Audio Language Models
Wei-Chih Chen, Chien-yu Huang, Hung-yi Lee
Despite the strong performance of large audio language models (LALMs) in various tasks, exactly how and where they integrate acoustic features with textual context remains unclear.…
Hearing the Order: Investigating Position Bias in Large Audio-Language Models
Yu-Xiang Lin, Chen-An Li, Sheng-Lun Wei +3
Large audio-language models (LALMs) are often used in tasks that involve reasoning over ordered options. An open question is whether their predictions are influenced by the order o…
When Silence Matters: The Impact of Irrelevant Audio on Text Reasoning in Large Audio-Language Models
Chen-An Li, Tzu-Han Lin, Hung-yi Lee
Large audio-language models (LALMs) unify speech and text processing, but their robustness in noisy real-world settings remains underexplored. We investigate how irrelevant audio,…