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 summarization. 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.

Enhancing Large Language Models-Based Code Generation by Leveraging Genetic Improvement

Giovanni Pinna;Damiano Ravalico;Luigi Rovito
;
Luca Manzoni;Andrea De Lorenzo
2024-01-01

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 summarization. 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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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11368/3071899
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