5 papers
Interpreting and Steering for Safe and Correct Code Generation
Hao Yan, Ziyu Yao
Large language models (LLMs) frequently generate source code containing vulnerabilities, yet little work studies the internal mechanisms that distinguish safe from vulnerable gener…
Why Do LLM-based Web Agents Fail? A Hierarchical Planning Perspective
Mohamed Aghzal, Gregory J. Stein, Ziyu Yao
Large language model (LLM) web agents are increasingly used for web navigation but remain far from human reliability on realistic, long-horizon tasks. Existing evaluations focus pr…
Guiding AI to Fix Its Own Flaws: An Empirical Study on LLM-Driven Secure Code Generation
Hao Yan, Swapneel Suhas Vaidya, Xiaokuan Zhang +1
Large Language Models have become powerful tools for programming. However, they often overlook essential security practices, producing insecure code with vulnerabilities. Despite t…
Can LLMs Simulate Personas with Reversed Performance? A Systematic Investigation for Counterfactual Instruction Following in Math Reasoning Context
Sai Adith Senthil Kumar, Hao Yan, Saipavan Perepa +2
Large Language Models (LLMs) are now increasingly widely used to simulate personas in virtual environments, leveraging their instruction-following capability. However, we discovere…
IntelliExplain: Enhancing Conversational Code Generation for Non-Professional Programmers
Hao Yan, Thomas D. Latoza, Ziyu Yao
Chat LLMs such as GPT-3.5-turbo and GPT-4 have shown promise in assisting humans in coding, particularly by enabling them to conversationally provide feedback. However, current app…