7 citations · 12 across the 19 of their papers we have counts for
4 papers · 1 filter
ArtistMus: A Globally Diverse, Artist-Centric Benchmark for Retrieval-Augmented Music Question Answering
Daeyong Kwon, SeungHeon Doh, Juhan Nam
Recent advances in large language models (LLMs) have transformed open-domain question answering, yet their effectiveness in music-related reasoning remains limited due to sparse mu…
MUST-RAG: MUSical Text Question Answering with Retrieval Augmented Generation
Daeyong Kwon, SeungHeon Doh, Juhan Nam
Recent advancements in Large language models (LLMs) have demonstrated remarkable capabilities across diverse domains. While they exhibit strong zero-shot performance on various tas…
Predicting User Intents and Musical Attributes from Music Discovery Conversations
Daeyong Kwon, SeungHeon Doh, Juhan Nam
Intent classification is a text understanding task that identifies user needs from input text queries. While intent classification has been extensively studied in various domains,…
K-pop Lyric Translation: Dataset, Analysis, and Neural-Modelling
Haven Kim, Jongmin Jung, Dasaem Jeong +1
Lyric translation, a field studied for over a century, is now attracting computational linguistics researchers. We identified two limitations in previous studies. Firstly, lyric tr…