Music-text multimodal systems have enabled new approaches to Music Information Research (MIR) applications. Despite the reported success, there has been little effort in evaluating the musical knowledge of Large Language Models (LLM). We demonstrate that LLMs suffer from prompt sensitivity, inability to model negation and sensitivity towards specific words. We quantified these properties as a triplet-based accuracy, evaluating the ability to model the relative similarity of labels in a hierarchical ontology. We leveraged Audioset ontology to generate triplets consisting of anchor, positive and negative label for genre/instruments sub-tree and use six general-purpose Transformer-based models. Triplets required filtering, as some were difficult to judge and therefore relatively uninformative for evaluation purposes. Despite the relatively high accuracy reported, inconsistencies are evident in all six models, suggesting that off-the-shelf LLMs need adaptation to music before use.

Conversational Agents (CAs) can be a proxy for disseminating information and providing support to the public, especially in times of crisis. CAs can scale to reach larger numbers of end-users than human operators, while they can offer information interactively and engagingly. In this work, we present Theano, a Greek-speaking virtual assistant for COVID-19. Theano presents users with COVID-19 statistics and facts and informs users about the best health practices as well as the latest COVID-19 related guidelines. Additionally, Theano provides support to end-users by helping them self-assess their symptoms and redirecting them to first-line health workers. The relevant, localized information that Theano provides, makes it a valuable tool for combating COVID-19 in Greece. Theano has already conversed with different users in more than 170 different conversations through a web interface as a chatbot and over the phone as a voice bot.