The reviewed record of science sign in
Pith

arxiv: 2402.16153 · v1 · pith:F2CWKXNQ · submitted 2024-02-25 · cs.SD · cs.AI· cs.CL· cs.LG· cs.MM· eess.AS

ChatMusician: Understanding and Generating Music Intrinsically with LLM

Reviewed by Pith T0 review T1 audit T2 compute T3 formal T4 kernel pith:F2CWKXNQrecord.jsonopen to challenge →

classification cs.SD cs.AIcs.CLcs.LGcs.MMeess.AS
keywords musicchatmusicianlanguageabilitiesmusicalllama2llmsmodel
0
0 comments X
read the original abstract

While Large Language Models (LLMs) demonstrate impressive capabilities in text generation, we find that their ability has yet to be generalized to music, humanity's creative language. We introduce ChatMusician, an open-source LLM that integrates intrinsic musical abilities. It is based on continual pre-training and finetuning LLaMA2 on a text-compatible music representation, ABC notation, and the music is treated as a second language. ChatMusician can understand and generate music with a pure text tokenizer without any external multi-modal neural structures or tokenizers. Interestingly, endowing musical abilities does not harm language abilities, even achieving a slightly higher MMLU score. Our model is capable of composing well-structured, full-length music, conditioned on texts, chords, melodies, motifs, musical forms, etc, surpassing GPT-4 baseline. On our meticulously curated college-level music understanding benchmark, MusicTheoryBench, ChatMusician surpasses LLaMA2 and GPT-3.5 on zero-shot setting by a noticeable margin. Our work reveals that LLMs can be an excellent compressor for music, but there remains significant territory to be conquered. We release our 4B token music-language corpora MusicPile, the collected MusicTheoryBench, code, model and demo in GitHub.

This paper has not been read by Pith yet.

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Forward citations

Cited by 2 Pith papers

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

  1. The Routing Plateau: Understanding and Breaking the Accuracy Limits of LLM Routers

    cs.LG 2026-05 unverdicted novelty 6.0

    LLM routers across 21 methods on 5 benchmarks converge to similar accuracy below oracle due to learning global performance trends rather than fine-grained query signals.

  2. Music Audio-Visual Question Answering Requires Specialized Multimodal Designs

    cs.SD 2025-05 unverdicted novelty 3.0

    Survey of Music AVQA finds specialized input processing, dedicated spatial-temporal designs, and music-specific modeling are critical for strong performance.