Detecting music entities such as song titles or artist names is a useful application to help use cases like processing music search queries or analyzing music consumption on the web. Recent approaches incorporate smaller language models (SLMs) like BERT and achieve high results. However, further research indicates a high influence of entity exposure during pre-training on the performance of the models. With the advent of large language models (LLMs), these outperform SLMs in a variety of downstream tasks. However, researchers are still divided if this is applicable to tasks like entity detection in texts due to issues like hallucination. In this paper, we provide a novel dataset of user-generated metadata and conduct a benchmark and a robustness study using recent LLMs with in-context-learning (ICL). Our results indicate that LLMs in the ICL setting yield higher performance than SLMs. We further uncover the large impact of entity exposure on the best performing LLM in our study.

翻译：检测歌曲名称或艺术家姓名等音乐实体是一项有价值的应用，有助于处理音乐搜索查询或分析网络音乐消费等用例。现有方法通常采用 BERT 等小型语言模型，并取得了优异效果。然而，进一步研究表明，预训练过程中的实体曝光度对模型性能具有显著影响。随着大型语言模型的出现，其在多种下游任务中已超越小型语言模型。但由于幻觉等问题，研究者对其在文本实体检测等任务中的适用性仍存分歧。本文构建了一个新颖的用户生成元数据集，并采用上下文学习策略对当前主流大型语言模型进行了基准测试与鲁棒性研究。实验结果表明，在上下文学习配置下，大型语言模型的性能优于小型语言模型。我们进一步揭示了实体曝光度对本研究中表现最佳的大型语言模型所产生的重大影响。