TW-Sound580K dataset plus Tai-LALM model with dynamic Dual-ASR arbitration lifts localized Taiwanese audio-language accuracy to 49.1% on the TAU benchmark.
When audio and text disagree: Reveal- ing text bias in large audio-language models
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Irrelevant audio including silence reduces accuracy and increases volatility in text reasoning for large audio-language models, with effects worsening at longer durations, higher amplitudes, and higher temperatures.
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TW-Sound580K: A Regional Audio-Text Dataset with Verification-Guided Curation for Localized Audio-Language Modeling
TW-Sound580K dataset plus Tai-LALM model with dynamic Dual-ASR arbitration lifts localized Taiwanese audio-language accuracy to 49.1% on the TAU benchmark.
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When Silence Matters: The Impact of Irrelevant Audio on Text Reasoning in Large Audio-Language Models
Irrelevant audio including silence reduces accuracy and increases volatility in text reasoning for large audio-language models, with effects worsening at longer durations, higher amplitudes, and higher temperatures.