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

• URL: http://arxiv.org/abs/2502.06769v1
• Date: Mon, 10 Feb 2025 18:48:45 GMT
• Title: Enhancing Trust in Language Model-Based Code Optimization through RLHF: A Research Design
• Authors: Jingzhi Gong,
• Abstract summary: 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.
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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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