Enhancing Large Language Models-Based Code Generation by Leveraging Genetic Improvement
Created by W.Langdon from
gp-bibliography.bib Revision:1.8098
- @InProceedings{Pinna:2024:EuroGP,
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author = "Giovanni Pinna and Damiano Ravalico and
Luigi Rovito and Luca Manzoni and Andrea {De Lorenzo}",
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editor = "Mario Giacobini and Bing Xue and Luca Manzoni",
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title = "Enhancing Large Language Models-Based Code Generation
by Leveraging Genetic Improvement",
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booktitle = "EuroGP 2024: Proceedings of the 27th European
Conference on Genetic Programming",
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year = "2024",
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volume = "14631",
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series = "LNCS",
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pages = "108--124",
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publisher = "Springer",
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address = "Aberystwyth",
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month = "3-5 " # apr,
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organisation = "EvoStar, Species",
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keywords = "genetic algorithms, genetic programming, Genetic
Improvement, Grammatical Evolution, Evolutionary
Computation, Evolutionary Algorithms, Large Language
Models, LLM, Artificial Intelligence, AI, Machine
Learning, Neural Networks, ANN, Code Generation",
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isbn13 = "978-3-031-56957-9",
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URL = "https://arts.units.it/handle/11368/3071899",
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DOI = "doi:10.1007/978-3-031-56957-9_7",
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code_url = "https://github.com/dravalico/LLMGIpy",
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size = "17 pages",
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abstract = "In recent years, the rapid advances in neural networks
for Natural Language Processing (NLP) have led to the
development of Large Language Models (LLMs), able to
substantially improve the state-of-the-art in many NLP
tasks, such as question answering and text
summarisation. Among them, one particularly interesting
application is automatic code generation based only on
the problem description. However, it has been shown
that even the most effective LLMs available often fail
to produce correct code. To address this issue, we
propose an evolutionary-based approach using Genetic
Improvement (GI) to improve the code generated by an
LLM using a collection of user-provided test cases.
Specifically, we employ Grammatical Evolution (GE)
using a grammar that we automatically specialize,
starting from a general one, for the output of the LLM.
We test 25 different problems and 5 different LLMs,
showing that the proposed method is able to improve in
a statistically significant way the code generated by
LLMs. This is a first step in showing that the
combination of LLMs and evolutionary techniques can be
a fruitful avenue of research.",
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notes = "Part of \cite{Giacobini:2024:GP} EuroGP'2024 held in
conjunction with EvoCOP2024, EvoMusArt2024 and
EvoApplications2024",
- }
Genetic Programming entries for
Giovanni Pinna
Damiano Ravalico
Luigi Rovito
Luca Manzoni
Andrea De Lorenzo
Citations