NeSy is alive and well: A LLM-driven symbolic approach for better code comment data generation and classification - Archive ouverte HAL Access content directly
Book Sections Year : 2024

NeSy is alive and well: A LLM-driven symbolic approach for better code comment data generation and classification

Abstract

We present a neuro-symbolic (NeSy) workflow combining a symbolic-based learning technique with a large language model (LLM) agent to generate synthetic data for code comment classification in the C programming language. We also show how generating controlled synthetic data using this workflow fixes some of the notable weaknesses of LLM-based generation and increases the performance of classical machine learning models on the code comment classification task. Our best model, a Neural Network, achieves a Macro-F1 score of 91.412% with an increase of 1.033% after data augmentation.
Fichier principal
Vignette du fichier
NeSy is alive and well - Hanna Abi Akl.pdf (668.21 Ko) Télécharger le fichier
Origin : Files produced by the author(s)
Licence : CC BY - Attribution

Dates and versions

hal-04481420 , version 1 (28-02-2024)

Identifiers

Cite

Hanna Abi Akl. NeSy is alive and well: A LLM-driven symbolic approach for better code comment data generation and classification. Springer Nature. Generative Artificial Intelligence for Code - Impact of Large Language Models on Code Generation and Summarization, Transactions on Computer Systems and Networks, , 2024, Transactions on Computer Systems and Networks. ⟨hal-04481420⟩
77 View
8 Download

Altmetric

Share

Gmail Facebook X LinkedIn More