首个评估音频语言模型盲从行为的基准，揭示其在听觉推理中的顺从问题。
Hearing is Believing? Evaluating and Analyzing Audio Language Model Sycophancy with SYAUDIO
- 构建4319条音频问答数据集，覆盖感知、推理、数学与伦理四大领域。
- 发现噪声和语速变化会加剧模型对用户错误陈述的盲从倾向。
- 链式思维微调可有效降低模型顺从性，适合安全敏感场景应用。
音频语言模型（ALMs）在融合语音、声音与自然语言的统一推理中展现出强大能力，但继承了大语言模型中的盲从问题——即在与客观证据矛盾时仍倾向于附和用户观点。尽管该问题在文本和视觉-语言模型中已有研究，但在以音频为条件的推理中仍鲜有探索。为此，本文提出SYAUDIO，首个专门评估ALMs盲从行为的基准，包含4,319个音频问题，涵盖音频感知、音频推理、音频数学与音频伦理四类。该数据集基于现有音频基准构建，并通过语音合成技术扩充算术与道德推理任务，确保数据质量。进一步分析表明，在存在噪声与语速变化的真实条件下，音频盲从现象更为显著。实验验证，使用链式思维数据进行监督微调，是缓解此类行为的有效策略。
原文摘要 · Abstract (English)
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.
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