Generative text-to-music models such as MusicGen are capable of generating high fidelity music conditioned on a text prompt. However, expressing the essential features of music with text is a challenging task. In this paper, we present a retrieval-augmented approach for text-to-music generation. We first pre-compute a dataset of text-music embeddings obtained from a contrastive language-audio pretrained encoder (CLAP). Then, given an input text prompt, we retrieve the top k most similar musical aspects and augment the original prompt. This approach consistently generates music of higher audio quality as measured by the Frechét Audio Distance. We analyze the internal representations of MusicGen and find that augmented prompts lead to greater diversity in token distributions and display high text adherence. Our findings show the potential for increased control in text-to-music generation.

Evaluations of model editing, a technique for changing the factual knowledge held by Large Language Models (LLMs), currently only use the ‘next few token’ completions after a prompt. As a result, the impact of these methods on longer natural language generation is largely unknown. We introduce long-form evaluation of model editing (LEME) a novel evaluation protocol that measures the efficacy and impact of model editing in long-form generative settings. Our protocol consists of a machine-rated survey and a classifier which correlates well with human ratings. Importantly, we find that our protocol has very little relationship with previous short-form metrics (despite being designed to extend efficacy, generalization, locality, and portability into a long-form setting), indicating that our method introduces a novel set of dimensions for understanding model editing methods. Using this protocol, we benchmark a number of model editing techniques and present several findings including that, while some methods (ROME and MEMIT) perform well in making consistent edits within a limited scope, they suffer much more from factual drift than other methods. Finally, we present a qualitative analysis that illustrates common failure modes in long-form generative settings including internal consistency, lexical cohesion, and locality issues.