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Retrieval Augmented Generation of Symbolic Music with LLMs

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arxiv 2311.10384 v2 pith:O2Q4Y6KK submitted 2023-11-17 cs.SD eess.AS

Nicolas Jonason , Luca Casini , Carl Thomé , Bob L.T. Sturm This is my paper · ORCID
classification cs.SDeess.AS
keywords generationmusicllmsretrievalsystemaugmentedavailablecode
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We explore the use of large language models (LLMs) for music generation using a retrieval system to select relevant examples. We find promising initial results for music generation in a dialogue with the user, especially considering the ease with which such a system can be implemented. The code is available online.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. AddrLLM: Address Rewriting via Large Language Model on Nationwide Logistics Data

    cs.CL 2024-11 conditional novelty 6.0 of 10

    An LLM fine-tuned on 60 million logistics address examples, with nearby-address retrieval and reinforcement learning from JD's geocoding service, rewrites abnormal Chinese addresses better than prior geocoding and rew...

  2. Improving Controllability and Editability for Pretrained Text-to-Music Generation Models

    cs.SD 2024-11 conditional novelty 2.0 of 10

    A thesis compilation presenting three complementary approaches to improving editing and control of pretrained text-to-music models, with Instruct-MusicGen demonstrating the strongest stem-level editing results.

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