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Autore principale: Gong, Jingzhi
Natura: Preprint
Pubblicazione: 2025
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Accesso online:https://arxiv.org/abs/2502.06769
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author Gong, Jingzhi
author_facet Gong, Jingzhi
contents With the rapid advancement of AI, software engineering increasingly relies on AI-driven approaches, particularly language models (LMs), to enhance code performance. However, the trustworthiness and reliability of LMs remain significant challenges due to the potential for hallucinations - unreliable or incorrect responses. To fill this gap, this research aims to develop reliable, LM-powered methods for code optimization that effectively integrate human feedback. This work aligns with the broader objectives of advancing cooperative and human-centric aspects of software engineering, contributing to the development of trustworthy AI-driven solutions.
format Preprint
id arxiv_https___arxiv_org_abs_2502_06769
institution arXiv
publishDate 2025
record_format arxiv
spellingShingle Enhancing Trust in Language Model-Based Code Optimization through RLHF: A Research Design
Gong, Jingzhi
Software Engineering
With the rapid advancement of AI, software engineering increasingly relies on AI-driven approaches, particularly language models (LMs), to enhance code performance. However, the trustworthiness and reliability of LMs remain significant challenges due to the potential for hallucinations - unreliable or incorrect responses. To fill this gap, this research aims to develop reliable, LM-powered methods for code optimization that effectively integrate human feedback. This work aligns with the broader objectives of advancing cooperative and human-centric aspects of software engineering, contributing to the development of trustworthy AI-driven solutions.
title Enhancing Trust in Language Model-Based Code Optimization through RLHF: A Research Design
topic Software Engineering
url https://arxiv.org/abs/2502.06769