Enhancing Trust in Language Model-Based Code Optimization through RLHF: A Research Design

Jingzhi Gong

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arXiv · 2502.06769

Enhancing Trust in Language Model-Based Code Optimization through RLHF: A Research Design

Jingzhi Gong

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Abstract

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.

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Jingzhi Gong. 2025-02-10. Enhancing Trust in Language Model-Based Code Optimization through RLHF: A Research Design. https://arxiv.org/abs/2502.06769

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