Towards Trustworthy LLMs for Code: A Data-Centric Synergistic Auditing Framework

Understand

LLM-powered coding and development assistants have become prevalent to programmers' workflows.

• However, concerns about the trustworthiness of LLMs for code persist despite their widespread use.
• Much of the existing research focused on either training or evaluation, raising questions about whether stakeholders in training and evaluation align in their understanding of model trustworthiness and whether they can move toward a unified direction.
• In this paper, we propose a vision for a unified trustworthiness auditing framework, DataTrust, which adopts a data-centric approach that synergistically emphasizes both training and evaluation data and their correlations.