Ziyu Yao, Assistant Professor, Computer Science, College of Engineering and Computing (CEC), received funding from the National Science Foundation for: “CAREER: Towards Scalable and Actionable Interpretability of Language Models for Code Generation.”
Yao seeks to advance AI interpretability research by addressing these two challenges and expanding scientific knowledge of language models (LM). In particular, the project focuses on code generation, a structured prediction task with broad applications in data analytics, information acquisition, software development, and beyond. The research outcomes will be integrated into a series of educational initiatives, including AI literacy outreach programs, undergraduate early research opportunities, new curriculum development, and open-source educational resources.
She has three goals for this research.
First, she will deliver a new framework for mechanistic LM interpretation. In addressing the scalability and actionability challenges, the framework interprets only a subset of LM mechanisms most relevant to downstream applications while employing LM agents to assist with the interpretation process.
Second, she will apply this framework to interpret LMs for code generation, investigating their mechanisms for syntactic rule application, semantic knowledge recalling, and deliberate reasoning, and will examine how these mechanisms can be impacted by data and learning paradigms.
Third, she will explore interpretation-informed methods to improve code generation, thereby translating interpretation insights into practical model enhancement.
Yao received $674,100 from NSF for this project. Funding began in July 2026 and will end in late June 2031.
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