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| Autores principales: | , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2026
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| Materias: | |
| Acceso en línea: | https://arxiv.org/abs/2601.23149 |
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| _version_ | 1866911411645448192 |
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| author | Yao, Junchi Lakshmikanthan, Lokranjan Zhao, Annie Zhao, Danielle Yang, Shu Ding, Zikang Wang, Di Hu, Lijie |
| author_facet | Yao, Junchi Lakshmikanthan, Lokranjan Zhao, Annie Zhao, Danielle Yang, Shu Ding, Zikang Wang, Di Hu, Lijie |
| contents | Audio Language Models (ALMs) have recently shown strong capabilities in unified reasoning over speech, sound, and natural language; yet they inherit behavioral issues observed in Large Language Models, including sycophancy--the tendency to agree with user assertions even when they contradict objective evidence. While sycophancy has been extensively studied in text and vision-language models, its manifestation in audio-conditioned reasoning remains largely unexplored, despite the need for ALMs to rely on auditory cues such as acoustic events, speaker characteristics, and speech rate. To address this gap, we introduce SYAUDIO, the first benchmark dedicated to evaluating sycophancy in ALMs, consisting of 4,319 audio questions spanning Audio Perception, Audio Reasoning, Audio Math, and Audio Ethics. Built upon established audio benchmarks and augmented with TTS-generated arithmetic and moral reasoning tasks, SYAUDIO enables systematic evaluation across multiple domains and sycophancy types with carefully verified data quality. Furthermore, we analyze audio-specific sycophancy under realistic conditions involving noise and rate, and demonstrate that supervised fine-tuning with chain-of-thought data is an effective mitigation strategy for reducing sycophantic behavior in ALMs. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_23149 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Hearing is Believing? Evaluating and Analyzing Audio Language Model Sycophancy with SYAUDIO Yao, Junchi Lakshmikanthan, Lokranjan Zhao, Annie Zhao, Danielle Yang, Shu Ding, Zikang Wang, Di Hu, Lijie Sound Audio Language Models (ALMs) have recently shown strong capabilities in unified reasoning over speech, sound, and natural language; yet they inherit behavioral issues observed in Large Language Models, including sycophancy--the tendency to agree with user assertions even when they contradict objective evidence. While sycophancy has been extensively studied in text and vision-language models, its manifestation in audio-conditioned reasoning remains largely unexplored, despite the need for ALMs to rely on auditory cues such as acoustic events, speaker characteristics, and speech rate. To address this gap, we introduce SYAUDIO, the first benchmark dedicated to evaluating sycophancy in ALMs, consisting of 4,319 audio questions spanning Audio Perception, Audio Reasoning, Audio Math, and Audio Ethics. Built upon established audio benchmarks and augmented with TTS-generated arithmetic and moral reasoning tasks, SYAUDIO enables systematic evaluation across multiple domains and sycophancy types with carefully verified data quality. Furthermore, we analyze audio-specific sycophancy under realistic conditions involving noise and rate, and demonstrate that supervised fine-tuning with chain-of-thought data is an effective mitigation strategy for reducing sycophantic behavior in ALMs. |
| title | Hearing is Believing? Evaluating and Analyzing Audio Language Model Sycophancy with SYAUDIO |
| topic | Sound |
| url | https://arxiv.org/abs/2601.23149 |