通过分析模型在音频错误下的混淆模式，优化音频问答数据集质量。
Learning from Audio-Dependency Errors: Data Curation Strategies Based on Model Confusion Patterns in Audio Question Answering
- 利用正常、无音频、乱序音频三种条件探测模型响应变化。
- 筛选出强依赖音频的样本，训练集准确率提升至67.27%。
- 适合做音频语言模型数据清洗与高质量训练集构建的研究者。
我们将系统视为面向大音频-语言模型的诊断性数据整理：在微调前，通过正常、无音频和音频乱序三种条件，探查Qwen3-Omni-30B-A3B-Instruct模型在音频证据缺失或错配时的答案变化。基于这些模型混淆模式，将训练样本划分为文本优先、乱序泄露、强音频依赖及困难/误导类案例。最强的仅训练系统仅使用强音频依赖样本（正常音频-问题对正确，但反事实变体均失败），并加入少量无音频负样本及文本仅响应归一化器以处理解析失败生成。在官方开发集上，经响应归一化后，最佳仅训练系统达到67.27%准确率，优于本地Qwen3-Omni基线的65.90%。最终提交还包含使用训练+开发集微调的模型及三模型集成。
原文摘要 · Abstract (English)
We frame the system as diagnostic data curation for a large audio-language model: before fine-tuning, we probe Qwen3-Omni-30B-A3B-Instruct under normal, empty-audio, and shuffled-audio conditions to identify how the model's answers change when audio evidence is removed or mismatched. These model confusion patterns are used to bucket training samples into text-prior, shuffle-leak, strong audio-dependent, and hard or misleading cases. Our strongest train-only system fine-tunes only on strong-audio items, where the normal audio-question pair is correct but both counterfactual variants fail, plus a small number of empty-audio negatives and a text-only response normalizer for parse-failed generations. On the official development set, the best train-only system reaches 67.27% accuracy after response normalization, compared with 65.90% for our local Qwen3-Omni baseline. Final submissions additionally include models trained using train+development splits and a three-model ensemble.
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