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

2025/02/10 by Jingzhi Gong, Gong, Jingzhi
Computer Science · #FOS: Computer and information sciences #Model-Driven Software Engineering Techniques #Software Engineering (cs.SE)

paper · pdf · doi:10.48550/arxiv.2502.06769

openalex publication_date 2025/02/10 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

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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