MusTBENCH：音乐大语言模型中的时间定位基准与推进
MusTBench: Benchmarking and Advancing Temporal Grounding in Music LLMs
AI总结：
提出MusTBENCH基准和MusT四阶段优化方法，评估并提升音乐大语言模型在音频中的时间定位能力。
摘要：
近期的大型音频-语言模型（LALMs）在理解音乐内容方面展现了有前景的能力。然而，它们的响应是否基于音频中正确的时间区域仍未得到充分探索。这一限制对于音乐理解尤为关键，因为关键信息通常以时间局部化事件的形式出现，例如乐器进入和节奏转换。为了解决这一差距，我们引入了MusTBENCH，一个由音乐专家验证的基准，旨在通过五个时间定位的问答任务评估LALMs中的时间定位能力。为了进一步提升现有模型中的时间定位，我们提出了MusT，一种新颖的四阶段时间优化方案，涵盖音乐编码器适应、LLM适应、LLM监督微调和基于RL的优化。在MusTBENCH上的实验表明，现有LALMs在精确时间定位方面存在困难，而MusT相比强基线带来了显著改进。这些结果将时间定位确立为当前LALMs中缺失的关键能力，并将MusTBENCH定位为未来时间定位音乐理解研究的具有挑战性的基准。
英文摘要：
Recent Large Audio-Language Models (LALMs) have demonstrated promising abilities in understanding musical content. However, whether their responses are grounded in the correct temporal regions of the audio remains underexplored. This limitation is particularly critical for music understanding, where key information often occurs as temporally localized events, such as instrument entries and rhythmic transitions. To address this gap, we introduce MusTBench, a music-expert-validated benchmark designed to evaluate temporal grounding in LALMs through five temporally grounded question-answering tasks. To further improve temporal grounding in existing models, we propose MusT, a novel four-stage temporal optimization recipe spanning music encoder adaptation, LLM adaptation, LLM supervised fine-tuning, and RL-based optimization. Experiments on MusTBench show that existing LALMs struggle with precise temporal grounding, while MusT brings significant improvements over strong baselines. These results establish temporal grounding as a key missing capability in current LALMs and position MusTBench as a challenging benchmark for future research in temporally grounded music understanding.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
