Synergistic Utilization of LLMs for Program Synthesis

Created by W.Langdon from gp-bibliography.bib Revision:1.7906

@InProceedings{vella-zarb:2024:GECCOcomp,

• author = "David {Vella Zarb} and Geoff Parks and Timoleon Kipouros",
• title = "Synergistic Utilization of {LLMs} for Program Synthesis",
• booktitle = "Proceedings of the 2024 Genetic and Evolutionary Computation Conference Companion",
• year = "2024",
• editor = "Ting Hu and Aniko Ekart",
• pages = "539--542",
• address = "Melbourne, Australia",
• series = "GECCO '24",
• month = "14-18 " # jul,
• organisation = "SIGEVO",
• publisher = "Association for Computing Machinery",
• publisher_address = "New York, NY, USA",
• keywords = "genetic algorithms, genetic programming, program synthesis, large language models, ANN, evolutionary algorithms: Poster",
• isbn13 = "979-8-4007-0495-6",
• DOI = "doi:10.1145/3638530.3654426",
• size = "4 pages",
• abstract = "Advances in Large Language Models (LLMs) have led them to be used as black boxes in several evolutionary algorithms for program synthesis. While these methods tend to be agnostic about which model is used, they only allow for using one. This paper suggests that using a combination of LLMs to seed population-based algorithms introduces more variation and leads to a wider variety of problems that can be solved, due to leveraging the strengths of component LLMs. We test this on the PSB2 suite, using the Search, Execute, Instruct, Debug and Rank (SEIDR) algorithm. In all cases examined, we find that using a combination of LLMs leads to more problems solved and better test pass rates compared to using the best individual model. We also find that the computational cost, as measured in terms of excess programs generated, is lowered.",
• notes = "GECCO-2024 GP A Recombination of the 33rd International Conference on Genetic Algorithms (ICGA) and the 29th Annual Genetic Programming Conference (GP)",

}

Genetic Programming entries for David Vella Zarb Geoff Parks Timoleon Kipouros

Citations