The detection of music entities such as songs or performing artists in natural language queries is an important task when designing conversational music recommendation agents. Previous research has observed the applicability of named entity recognition approaches for this task based on pre-trained encoders like BERT. In recent years, large language models (LLMs) have surpassed these encoders in a variety of downstream tasks. In this paper, we validate the use of LLMs for information extraction of music entities in conversational queries by few-shot prompting. We test different numbers of examples and compare two sampling methods to obtain few-shot examples. Our results indicate that LLM performance can achieve state-of-the-art performance in the task.

YouTube is a rich source of cover songs. Since the platform itself is organized in terms of videos rather than songs, the retrieval of covers is not trivial. The field of cover song identification addresses this problem and provides approaches that usually rely on audio content. However, including the user-generated video metadata available on YouTube promises improved identification results. In this paper, we propose a multi-modal approach for cover song identification on online video platforms. We combine the entity resolution models with audio-based approaches using a ranking model. Our findings implicate that leveraging user-generated metadata can stabilize cover song identification performance on YouTube.