Built on

O’Neill, M., Vanneschi, L., Gustafson, S., Banzhaf, W.: Open issues in genetic programming. Genetic Programming and Evolvable Machines 11
2010
Earlier work this paper cites.
Griffith, S., Subramanian, K., Scholz, J., Isbell, C.L., Thomaz, A.L.: Policy shaping: Integrating human feedback with reinforcement learning. Advances in neural information processing systems 26
2013
Earlier work this paper cites.
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010. Curran Associates Inc., Red Hook, NY, USA (2017)
2017
Earlier work this paper cites.
Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al
2019
Earlier work this paper cites.
O’Neill, M., Spector, L.: Automatic programming: The open issue? Genetic Programming and Evolvable Machines 21
2020
Earlier work this paper cites.
Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: Language Models are Few-Shot Learners (2020)
2020
Earlier work this paper cites.
Strubell, E., Ganesh, A., McCallum, A.: Energy and policy considerations for modern deep learning research. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, pp. 13693–13696 (2020)
2020
Earlier work this paper cites.
Raji, I.D., Gebru, T., Mitchell, M., Buolamwini, J., Lee, J., Denton, E.: Saving Face: Investigating the Ethical Concerns of Facial Recognition Auditing (2020)
2020
Earlier work this paper cites.
Patterson, D., Gonzalez, J., Le, Q., Liang, C., Munguia, L.-M., Rothchild, D., So, D., Texier, M., Dean, J.: Carbon emissions and large neural network training. arXiv preprint arXiv:2104.10350 (2021)
Original
2021
Earlier work this paper cites.
Phuong, M., Hutter, M.: Formal algorithms for transformers. arXiv preprint arXiv:2207.09238 (2022)
Original
2022
Earlier work this paper cites.
Wu, C.-J., Raghavendra, R., Gupta, U., Acun, B., Ardalani, N., Maeng, K., Chang, G., Aga, F., Huang, J., Bai, C., et al
2022
Earlier work this paper cites.
Kaack, L.H., Donti, P.L., Strubell, E., Kamiya, G., Creutzig, F., Rolnick, D.: Aligning artificial intelligence with climate change mitigation. Nature Climate Change 12
2022
Earlier work this paper cites.
Zhou, H., Nova, A., Larochelle, H., Courville, A., Neyshabur, B., Sedghi, H.: Teaching algorithmic reasoning via in-context learning. arXiv preprint arXiv:2211.09066 (2022)
Original
2022
Earlier work this paper cites.
Izacard, G., Lewis, P., Lomeli, M., Hosseini, L., Petroni, F., Schick, T., Dwivedi-Yu, J., Joulin, A., Riedel, S., Grave, E.: Few-shot learning with retrieval augmented language models. arXiv preprint arXiv:2208.03299 (2022)
Original
2022
Earlier work this paper cites.
Wei, J., Wang, X., Schuurmans, D., Bosma, M., Chi, E., Le, Q., Zhou, D.: Chain of thought prompting elicits reasoning in large language models. arXiv preprint arXiv:2201.11903 (2022)
Original
2022
Earlier work this paper cites.
Lehman, J., Gordon, J., Jain, S., Ndousse, K., Yeh, C., Stanley, K.O.: Evolution through large models. arXiv preprint arXiv:2206.08896 (2022)
Original
2022
Earlier work this paper cites.
Helmuth, T., Kelly, P.: Applying genetic programming to psb2: the next generation program synthesis benchmark suite. Genetic Programming and Evolvable Machines 23
2022
Earlier work this paper cites.

Similar

Webson, A., Pavlick, E.: Do prompt-based models really understand the meaning of their prompts? In: Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp. 2300–2344. Association for Computational Linguistics, Seattle, United States (2022). https://doi.org/10.18653/v1/2022.naacl-main.167 . https://aclanthology.org/2022.naacl-main.167
2022
Cited alongside, same era.
Chen, A., Dohan, D.M., So, D.R.: Evoprompting: Language models for code-level neural architecture search. arXiv preprint arXiv:2302.14838 (2023)
Original
2023
Cited alongside, same era.
Liventsev, V., Grishina, A., Härmä, A., Moonen, L.: Fully autonomous programming with large language models. arXiv preprint arXiv:2304.10423 (2023)
Original
2023
Cited alongside, same era.
Liu, P., Yuan, W., Fu, J., Jiang, Z., Hayashi, H., Neubig, G.: Pre-train, prompt, and predict: A systematic survey of prompting methods in natural language processing. ACM Computing Surveys 55

Then

Connectionists: Chomsky’s apple. https://mailman.srv.cs.cmu.edu/pipermail/connectionists/2023-March/039546.html . Accessed: 2023-10-27
2023
Later among the works it cites.
Roziere, B., Gehring, J., Gloeckle, F., Sootla, S., Gat, I., Tan, X.E., Adi, Y., Liu, J., Remez, T., Rapin, J., et al.: Code llama: Open foundation models for code. arXiv preprint arXiv:2308.12950 (2023)
Original
2023
Later among the works it cites.
Preparatory Steps of Genetic Programming. http://www.genetic-programming.com/gppreparatory.html . Accessed: 2023-10-27
2023
Later among the works it cites.
Ling, T., Chen, L., Lai, Y., Liu, H.-L.: Evolutionary Verbalizer Search for Prompt-based Few Shot Text Classification (2023)
2023
Later among the works it cites.
Zelikman, E., Lorch, E., Mackey, L., Kalai, A.T.: Self-Taught Optimizer (STOP): Recursively Self-Improving Code Generation (2023)
2023

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2024

Evolving Code with A Large Language Model

Hemberg, Erik, Moskal, Stephen, O'Reilly, Una-May

Understand

Algorithms that use Large Language Models (LLMs) to evolve code arrived on the Genetic Programming (GP) scene very recently.

We present LLM GP, a formalized LLM-based evolutionary algorithm designed to evolve code.
Like GP, it uses evolutionary operators, but its designs and implementations of those operators radically differ from GP's because they enlist an LLM, using prompting and the LLM's pre-trained pattern matching and sequence completion capability.
We also present a demonstration-level variant of LLM GP and share its code.

Built on

O’Neill, M., Vanneschi, L., Gustafson, S., Banzhaf, W.: Open issues in genetic programming. Genetic Programming and Evolvable Machines 11
2010
Earlier work this paper cites.
Griffith, S., Subramanian, K., Scholz, J., Isbell, C.L., Thomaz, A.L.: Policy shaping: Integrating human feedback with reinforcement learning. Advances in neural information processing systems 26
2013
Earlier work this paper cites.
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010. Curran Associates Inc., Red Hook, NY, USA (2017)
2017
Earlier work this paper cites.
Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al
2019
Earlier work this paper cites.
O’Neill, M., Spector, L.: Automatic programming: The open issue? Genetic Programming and Evolvable Machines 21
2020
Earlier work this paper cites.
Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: Language Models are Few-Shot Learners (2020)
2020
Earlier work this paper cites.
Strubell, E., Ganesh, A., McCallum, A.: Energy and policy considerations for modern deep learning research. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, pp. 13693–13696 (2020)
2020
Earlier work this paper cites.
Raji, I.D., Gebru, T., Mitchell, M., Buolamwini, J., Lee, J., Denton, E.: Saving Face: Investigating the Ethical Concerns of Facial Recognition Auditing (2020)
2020
Earlier work this paper cites.
Patterson, D., Gonzalez, J., Le, Q., Liang, C., Munguia, L.-M., Rothchild, D., So, D., Texier, M., Dean, J.: Carbon emissions and large neural network training. arXiv preprint arXiv:2104.10350 (2021)
Original
2021
Earlier work this paper cites.
Phuong, M., Hutter, M.: Formal algorithms for transformers. arXiv preprint arXiv:2207.09238 (2022)
Original
2022
Earlier work this paper cites.
Wu, C.-J., Raghavendra, R., Gupta, U., Acun, B., Ardalani, N., Maeng, K., Chang, G., Aga, F., Huang, J., Bai, C., et al
2022
Earlier work this paper cites.
Kaack, L.H., Donti, P.L., Strubell, E., Kamiya, G., Creutzig, F., Rolnick, D.: Aligning artificial intelligence with climate change mitigation. Nature Climate Change 12
2022
Earlier work this paper cites.
Zhou, H., Nova, A., Larochelle, H., Courville, A., Neyshabur, B., Sedghi, H.: Teaching algorithmic reasoning via in-context learning. arXiv preprint arXiv:2211.09066 (2022)
Original
2022
Earlier work this paper cites.
Izacard, G., Lewis, P., Lomeli, M., Hosseini, L., Petroni, F., Schick, T., Dwivedi-Yu, J., Joulin, A., Riedel, S., Grave, E.: Few-shot learning with retrieval augmented language models. arXiv preprint arXiv:2208.03299 (2022)
Original
2022
Earlier work this paper cites.
Wei, J., Wang, X., Schuurmans, D., Bosma, M., Chi, E., Le, Q., Zhou, D.: Chain of thought prompting elicits reasoning in large language models. arXiv preprint arXiv:2201.11903 (2022)
Original
2022
Earlier work this paper cites.
Lehman, J., Gordon, J., Jain, S., Ndousse, K., Yeh, C., Stanley, K.O.: Evolution through large models. arXiv preprint arXiv:2206.08896 (2022)
Original
2022
Earlier work this paper cites.
Helmuth, T., Kelly, P.: Applying genetic programming to psb2: the next generation program synthesis benchmark suite. Genetic Programming and Evolvable Machines 23
2022
Earlier work this paper cites.

Similar

Webson, A., Pavlick, E.: Do prompt-based models really understand the meaning of their prompts? In: Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp. 2300–2344. Association for Computational Linguistics, Seattle, United States (2022). https://doi.org/10.18653/v1/2022.naacl-main.167 . https://aclanthology.org/2022.naacl-main.167
2022
Cited alongside, same era.
Chen, A., Dohan, D.M., So, D.R.: Evoprompting: Language models for code-level neural architecture search. arXiv preprint arXiv:2302.14838 (2023)
Original
2023
Cited alongside, same era.
Liventsev, V., Grishina, A., Härmä, A., Moonen, L.: Fully autonomous programming with large language models. arXiv preprint arXiv:2304.10423 (2023)
Original
2023
Cited alongside, same era.
Liu, P., Yuan, W., Fu, J., Jiang, Z., Hayashi, H., Neubig, G.: Pre-train, prompt, and predict: A systematic survey of prompting methods in natural language processing. ACM Computing Surveys 55

Then

Connectionists: Chomsky’s apple. https://mailman.srv.cs.cmu.edu/pipermail/connectionists/2023-March/039546.html . Accessed: 2023-10-27
2023
Later among the works it cites.
Roziere, B., Gehring, J., Gloeckle, F., Sootla, S., Gat, I., Tan, X.E., Adi, Y., Liu, J., Remez, T., Rapin, J., et al.: Code llama: Open foundation models for code. arXiv preprint arXiv:2308.12950 (2023)
Original
2023
Later among the works it cites.
Preparatory Steps of Genetic Programming. http://www.genetic-programming.com/gppreparatory.html . Accessed: 2023-10-27
2023
Later among the works it cites.
Ling, T., Chen, L., Lai, Y., Liu, H.-L.: Evolutionary Verbalizer Search for Prompt-based Few Shot Text Classification (2023)
2023
Later among the works it cites.
Zelikman, E., Lorch, E., Mackey, L., Kalai, A.T.: Self-Taught Optimizer (STOP): Recursively Self-Improving Code Generation (2023)
2023

Beyond the bibliography

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

Open on alphaXiv

alphaXiv is searching for related work…

2023

Cited alongside, same era.

OpenAI: GPT-4 Technical Report (2023)

2023

Cited alongside, same era.

Ji, Z., Lee, N., Frieske, R., Yu, T., Su, D., Xu, Y., Ishii, E., Bang, Y.J., Madotto, A., Fung, P.: Survey of hallucination in natural language generation. ACM Comput. Surv. 55

2023

Cited alongside, same era.

Wang, X., Wei, J., Schuurmans, D., Le, Q., Chi, E., Narang, S., Chowdhery, A., Zhou, D.: Self-Consistency Improves Chain of Thought Reasoning in Language Models (2023)

2023

Cited alongside, same era.

Shao, Z., Gong, Y., Shen, Y., Huang, M., Duan, N., Chen, W.: Synthetic prompting: Generating chain-of-thought demonstrations for large language models. arXiv preprint arXiv:2302.00618 (2023)

Original

2023

Cited alongside, same era.

Yao, S., Yu, D., Zhao, J., Shafran, I., Griffiths, T.L., Cao, Y., Narasimhan, K.: Tree of thoughts: Deliberate problem solving with large language models. arXiv preprint arXiv:2305.10601 (2023)

Original

2023

Cited alongside, same era.

Appel, G., Neelbauer, J., Schweidel, D.: Generative ai has an intellectual property problem. april 07, 2023. Harvard Business Review (2023)

2023

Cited alongside, same era.

Chen, L., Zaharia, M., Zou, J.: How is chatgpt’s behavior changing over time? arXiv preprint arXiv:2307.09009 (2023)

Original

2023

Cited alongside, same era.

Du, Y., Li, S., Torralba, A., Tenenbaum, J.B., Mordatch, I.: Improving factuality and reasoning in language models through multiagent debate. arXiv preprint arXiv:2305.14325 (2023)

Original

2023

Cited alongside, same era.