Built on

——, “Music, evolution and language,” Developmental science , vol. 10, no. 1, pp. 35–39, 2007
2007
Earlier work this paper cites.
N. Masataka, “The origins of language and the evolution of music: A comparative perspective,” Physics of Life Reviews , vol. 6, no. 1, pp. 11–22, 2009
2009
Earlier work this paper cites.
E. H. Margulis and R. Simchy-Gross, “Repetition enhances the musicality of randomly generated tone sequences,” Music Perception: An Interdisciplinary Journal , vol. 33, no. 4, pp. 509–514, 2016
2016
Earlier work this paper cites.
A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, Ł. Kaiser, and I. Polosukhin, “Attention is all you need,” Advances in neural information processing systems , vol. 30, 2017
2017
Earlier work this paper cites.
C.-Z. A. Huang, A. Vaswani, J. Uszkoreit, N. Shazeer, I. Simon, C. Hawthorne, A. M. Dai, M. D. Hoffman, M. Dinculescu, and D. Eck, “Music transformer,” arXiv preprint arXiv:1809.04281 , 2018
Original
2018
Earlier work this paper cites.
C. Payne, “Musenet,” OpenAI Blog, Apr 2019. [Online]. Available: https://openai.com/blog/musenet
2019
Earlier work this paper cites.
H. Jhamtani and T. Berg-Kirkpatrick, “Modeling self-repetition in music generation using generative adversarial networks,” in Machine Learning for Music Discovery Workshop, ICML , 2019
2019
Earlier work this paper cites.
P. Dhariwal, H. Jun, C. Payne, J. W. Kim, A. Radford, and I. Sutskever, “Jukebox: A generative model for music,” arXiv preprint arXiv:2005.00341 , 2020
Original
2020
Earlier work this paper cites.
S. Dai, H. Yu, and R. B. Dannenberg, “What is missing in deep music generation? a study of repetition and structure in popular music,” arXiv preprint arXiv:2209.00182 , 2022
Original
2022
Earlier work this paper cites.
M. C. Pino, M. Giancola, and S. D’Amico, “The association between music and language in children: A state-of-the-art review,” Children , vol. 10, no. 5, p. 801, 2023
2023
Earlier work this paper cites.

Similar

P. Lu, X. Xu, C. Kang, B. Yu, C. Xing, X. Tan, and J. Bian, “Musecoco: Generating symbolic music from text,” 2023
2023
Cited alongside, same era.
A. Agostinelli, T. I. Denk, Z. Borsos, J. Engel, M. Verzetti, A. Caillon, Q. Huang, A. Jansen, A. Roberts, M. Tagliasacchi et al. , “Musiclm: Generating music from text,” arXiv preprint arXiv:2301.11325 , 2023
Original
2023
Cited alongside, same era.
J. Copet, F. Kreuk, I. Gat, T. Remez, D. Kant, G. Synnaeve, Y. Adi, and A. Défossez, “Simple and controllable music generation,” arXiv preprint arXiv:2306.05284 , 2023
Original
2023
Cited alongside, same era.
X. Yue, X. Qu, G. Zhang, Y. Fu, W. Huang, H. Sun, Y. Su, and W. Chen, “Mammoth: Building math generalist models through hybrid instruction tuning,” arXiv preprint arXiv:2309.05653 , 2023
Original
2023
Cited alongside, same era.
B. Roziere, J. Gehring, F. Gloeckle, S. Sootla, I. Gat, X. E. Tan, Y. Adi, J. Liu, T. Remez, J. Rapin et al. , “Code llama: Open foundation models for code,” arXiv preprint arXiv:2308.12950 , 2023

Then

D. Yu, K. Song, P. Lu, T. He, X. Tan, W. Ye, S. Zhang, and J. Bian, “Musicagent: An ai agent for music understanding and generation with large language models,” arXiv preprint arXiv:2310.11954 , 2023
Original
2023
Later among the works it cites.
Y. Wang, Y. Kordi, S. Mishra, A. Liu, N. A. Smith, D. Khashabi, and H. Hajishirzi, “Self-instruct: Aligning language models with self-generated instructions,” 2023
2023
Later among the works it cites.
J. Wei, X. Wang, D. Schuurmans, M. Bosma, B. Ichter, F. Xia, E. Chi, Q. Le, and D. Zhou, “Chain-of-thought prompting elicits reasoning in large language models,” 2023
2023
Later among the works it cites.
Q. Wu, G. Bansal, J. Zhang, Y. Wu, B. Li, E. Zhu, L. Jiang, X. Zhang, S. Zhang, J. Liu, A. H. Awadallah, R. W. White, D. Burger, and C. Wang, “Autogen: Enabling next-gen llm applications via multi-agent conversation,” 2023
2023
Later among the works it cites.

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2024

ComposerX: Multi-Agent Symbolic Music Composition with LLMs

Deng, Qixin, Yang, Qikai, Yuan, Ruibin et al.

Understand

Music composition represents the creative side of humanity, and itself is a complex task that requires abilities to understand and generate information with long dependency and harmony constraints.

While demonstrating impressive capabilities in STEM subjects, current LLMs easily fail in this task, generating ill-written music even when equipped with modern techniques like In-Context-Learning and Chain-of-Thoughts.
To further explore and enhance LLMs' potential in music composition by leveraging their reasoning ability and the large knowledge base in music history and theory, we propose ComposerX, an agent-based symbolic music generation framework.
We find that applying a multi-agent approach significantly improves the music composition quality of GPT-4.

Built on

——, “Music, evolution and language,” Developmental science , vol. 10, no. 1, pp. 35–39, 2007
2007
Earlier work this paper cites.
N. Masataka, “The origins of language and the evolution of music: A comparative perspective,” Physics of Life Reviews , vol. 6, no. 1, pp. 11–22, 2009
2009
Earlier work this paper cites.
E. H. Margulis and R. Simchy-Gross, “Repetition enhances the musicality of randomly generated tone sequences,” Music Perception: An Interdisciplinary Journal , vol. 33, no. 4, pp. 509–514, 2016
2016
Earlier work this paper cites.
A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, Ł. Kaiser, and I. Polosukhin, “Attention is all you need,” Advances in neural information processing systems , vol. 30, 2017
2017
Earlier work this paper cites.
C.-Z. A. Huang, A. Vaswani, J. Uszkoreit, N. Shazeer, I. Simon, C. Hawthorne, A. M. Dai, M. D. Hoffman, M. Dinculescu, and D. Eck, “Music transformer,” arXiv preprint arXiv:1809.04281 , 2018
Original
2018
Earlier work this paper cites.
C. Payne, “Musenet,” OpenAI Blog, Apr 2019. [Online]. Available: https://openai.com/blog/musenet
2019
Earlier work this paper cites.
H. Jhamtani and T. Berg-Kirkpatrick, “Modeling self-repetition in music generation using generative adversarial networks,” in Machine Learning for Music Discovery Workshop, ICML , 2019
2019
Earlier work this paper cites.
P. Dhariwal, H. Jun, C. Payne, J. W. Kim, A. Radford, and I. Sutskever, “Jukebox: A generative model for music,” arXiv preprint arXiv:2005.00341 , 2020
Original
2020
Earlier work this paper cites.
S. Dai, H. Yu, and R. B. Dannenberg, “What is missing in deep music generation? a study of repetition and structure in popular music,” arXiv preprint arXiv:2209.00182 , 2022
Original
2022
Earlier work this paper cites.
M. C. Pino, M. Giancola, and S. D’Amico, “The association between music and language in children: A state-of-the-art review,” Children , vol. 10, no. 5, p. 801, 2023
2023
Earlier work this paper cites.

Similar

P. Lu, X. Xu, C. Kang, B. Yu, C. Xing, X. Tan, and J. Bian, “Musecoco: Generating symbolic music from text,” 2023
2023
Cited alongside, same era.
A. Agostinelli, T. I. Denk, Z. Borsos, J. Engel, M. Verzetti, A. Caillon, Q. Huang, A. Jansen, A. Roberts, M. Tagliasacchi et al. , “Musiclm: Generating music from text,” arXiv preprint arXiv:2301.11325 , 2023
Original
2023
Cited alongside, same era.
J. Copet, F. Kreuk, I. Gat, T. Remez, D. Kant, G. Synnaeve, Y. Adi, and A. Défossez, “Simple and controllable music generation,” arXiv preprint arXiv:2306.05284 , 2023
Original
2023
Cited alongside, same era.
X. Yue, X. Qu, G. Zhang, Y. Fu, W. Huang, H. Sun, Y. Su, and W. Chen, “Mammoth: Building math generalist models through hybrid instruction tuning,” arXiv preprint arXiv:2309.05653 , 2023
Original
2023
Cited alongside, same era.
B. Roziere, J. Gehring, F. Gloeckle, S. Sootla, I. Gat, X. E. Tan, Y. Adi, J. Liu, T. Remez, J. Rapin et al. , “Code llama: Open foundation models for code,” arXiv preprint arXiv:2308.12950 , 2023

Then

D. Yu, K. Song, P. Lu, T. He, X. Tan, W. Ye, S. Zhang, and J. Bian, “Musicagent: An ai agent for music understanding and generation with large language models,” arXiv preprint arXiv:2310.11954 , 2023
Original
2023
Later among the works it cites.
Y. Wang, Y. Kordi, S. Mishra, A. Liu, N. A. Smith, D. Khashabi, and H. Hajishirzi, “Self-instruct: Aligning language models with self-generated instructions,” 2023
2023
Later among the works it cites.
J. Wei, X. Wang, D. Schuurmans, M. Bosma, B. Ichter, F. Xia, E. Chi, Q. Le, and D. Zhou, “Chain-of-thought prompting elicits reasoning in large language models,” 2023
2023
Later among the works it cites.
Q. Wu, G. Bansal, J. Zhang, Y. Wu, B. Li, E. Zhu, L. Jiang, X. Zhang, S. Zhang, J. Liu, A. H. Awadallah, R. W. White, D. Burger, and C. Wang, “Autogen: Enabling next-gen llm applications via multi-agent conversation,” 2023
2023
Later among the works it cites.

Beyond the bibliography

alphaXiv searches the wider corpus for related work and actual follow-ups.

Open on alphaXiv

alphaXiv is searching for related work…

Original

2023

Cited alongside, same era.

S. Bubeck, V. Chandrasekaran, R. Eldan, J. Gehrke, E. Horvitz, E. Kamar, P. Lee, Y. T. Lee, Y. Li, S. Lundberg et al. , “Sparks of artificial general intelligence: Early experiments with gpt-4,” arXiv preprint arXiv:2303.12712 , 2023

Original

2023

Cited alongside, same era.

L. Lin, G. Xia, J. Jiang, and Y. Zhang, “Content-based controls for music large language modeling,” arXiv preprint arXiv:2310.17162 , 2023

Original

2023

Cited alongside, same era.

——, “Equipping musicgen with chord and rhythm controls,” in Ismir 2023 Hybrid Conference , 2023

2023

Cited alongside, same era.

S. Wu and M. Sun, “Exploring the efficacy of pre-trained checkpoints in text-to-music generation task,” 2023

2023

Cited alongside, same era.

Y. Zhang, A. Maezawa, G. Xia, K. Yamamoto, and S. Dixon, “Loop copilot: Conducting ai ensembles for music generation and iterative editing,” arXiv preprint arXiv:2310.12404 , 2023

Original

2023

Cited alongside, same era.

C. Donahue, A. Caillon, A. Roberts, E. Manilow, P. Esling, A. Agostinelli, M. Verzetti, I. Simon, O. Pietquin, N. Zeghidour et al. , “Singsong: Generating musical accompaniments from singing,” arXiv preprint arXiv:2301.12662 , 2023

Original

2023

Later among the works it cites.

X. Qu, Y. Bai, Y. Ma, Z. Zhou, K. M. Lo, J. Liu, R. Yuan, L. Min, X. Liu, T. Zhang et al. , “Mupt: A generative symbolic music pretrained transformer,” arXiv preprint arXiv:2404.06393 , 2024

Original

2024

Closest in time.

L. Lin, G. Xia, Y. Zhang, and J. Jiang, “Arrange, inpaint, and refine: Steerable long-term music audio generation and editing via content-based controls,” arXiv preprint arXiv:2402.09508 , 2024

Original

2024

Closest in time.

R. Yuan, H. Lin, Y. Wang, Z. Tian, S. Wu, T. Shen, G. Zhang, Y. Wu, C. Liu, Z. Zhou, Z. Ma, L. Xue, Z. Wang, Q. Liu, T. Zheng, Y. Li, Y. Ma, Y. Liang, X. Chi, R. Liu, Z. Wang, P. Li, J. Wu, C. Lin, Q. Liu, T. Jiang, W. Huang, W. Chen, E. Benetos, J. Fu, G. Xia, R. Dannenberg, W. Xue, S. Kang, and Y. Guo, “Chatmusician: Understanding and generating music intrinsically with llm,” 2024

2024

Closest in time.

S. Ding, Z. Liu, X. Dong, P. Zhang, R. Qian, C. He, D. Lin, and J. Wang, “Songcomposer: A large language model for lyric and melody composition in song generation,” arXiv preprint arXiv:2402.17645 , 2024

Original

2024

Closest in time.

X. Liang, J. Lin, and X. Du, “Bytecomposer: a human-like melody composition method based on language model agent,” arXiv preprint arXiv:2402.17785 , 2024

Original

2024

Closest in time.