  We present a study of Tip-of-the-tongue (ToT) retrieval for music, where a
searcher is trying to find an existing music entity, but is unable to succeed
as they cannot accurately recall important identifying information. ToT
information needs are characterized by complexity, verbosity, uncertainty, and
possible false memories. We make four contributions. (1) We collect a dataset -
$ToT_{Music}$ - of 2,278 information needs and ground truth answers. (2) We
introduce a schema for these information needs and show that they often involve
multiple modalities encompassing several Music IR subtasks such as lyric
search, audio-based search, audio fingerprinting, and text search. (3) We
underscore the difficulty of this task by benchmarking a standard text
retrieval approach on this dataset. (4) We investigate the efficacy of query
reformulations generated by a large language model (LLM), and show that they
are not as effective as simply employing the entire information need as a query
- leaving several open questions for future research.
