WildScore: 多模态符号音乐推理在现实中的基准测试
WildScore: Benchmarking MLLMs in-the-Wild Symbolic Music Reasoning
AI总结：
WildScore是首个用于评估多模态大语言模型在现实世界中符号音乐推理能力的基准测试，通过真实音乐作品和用户生成问题，系统性评估模型在音乐学分析中的表现。
摘要：
近年来，多模态大语言模型（MLLMs）在各种视觉-语言任务中展示了令人印象深刻的能力。然而，它们在多模态符号音乐领域的推理能力仍然鲜有探索。我们介绍了WildScore，首个在现实世界中用于多模态符号音乐推理和分析的基准测试，旨在评估MLLMs解读真实音乐谱和回答复杂音乐学问题的能力。WildScore中的每个实例均来源于真实的音乐作品，并配有真实的用户生成问题和讨论，捕捉了实际音乐分析的复杂性。为了促进系统评估，我们提出了一个系统性的分类法，包括高层次和细粒度的音乐学本体。此外，我们将复杂的音乐推理框架为多项选择题回答，使能够对MLLMs的符号音乐理解进行受控和可扩展的评估。对最新最先进的MLLMs在WildScore上的实证基准测试揭示了其视觉-符号推理中的有趣模式，揭示了MLLMs在符号音乐推理和分析中的有前途的方向和持续的挑战。我们发布了数据集和代码。
英文摘要：
Recent advances in Multimodal Large Language Models (MLLMs) have demonstrated impressive capabilities across various vision-language tasks. However, their reasoning abilities in the multimodal symbolic music domain remain largely unexplored. We introduce WildScore, the first in-the-wild multimodal symbolic music reasoning and analysis benchmark, designed to evaluate MLLMs' capacity to interpret real-world music scores and answer complex musicological queries. Each instance in WildScore is sourced from genuine musical compositions and accompanied by authentic user-generated questions and discussions, capturing the intricacies of practical music analysis. To facilitate systematic evaluation, we propose a systematic taxonomy, comprising both high-level and fine-grained musicological ontologies. Furthermore, we frame complex music reasoning as multiple-choice question answering, enabling controlled and scalable assessment of MLLMs' symbolic music understanding. Empirical benchmarking of state-of-the-art MLLMs on WildScore reveals intriguing patterns in their visual-symbolic reasoning, uncovering both promising directions and persistent challenges for MLLMs in symbolic music reasoning and analysis. We release the dataset and code.
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。
