2 citations · 4 across the 6 of their papers we have counts for
9 papers
Evaluating Implicit Regulatory Compliance in LLM Tool Invocation via Logic-Guided Synthesis
Da Song, Yuheng Huang, Boqi Chen +4
The integration of large language models (LLMs) into autonomous agents has enabled complex tool use, yet in high-stakes domains, these systems must strictly adhere to regulatory st…
TRUSTVIS: A Multi-Dimensional Trustworthiness Evaluation Framework for Large Language Models
Ruoyu Sun, Da Song, Jiayang Song +2
As Large Language Models (LLMs) continue to revolutionize Natural Language Processing (NLP) applications, critical concerns about their trustworthiness persist, particularly in saf…
Evaluating LLMs on Sequential API Call Through Automated Test Generation
Yuheng Huang, Jiayang Song, Da Song +4
By integrating tools from external APIs, Large Language Models (LLMs) have expanded their promising capabilities in a diverse spectrum of complex real-world tasks. However, testing…
Risk Assessment Framework for Code LLMs via Leveraging Internal States
Yuheng Huang, Lei Ma, Keizaburo Nishikino +1
The pre-training paradigm plays a key role in the success of Large Language Models (LLMs), which have been recognized as one of the most significant advancements of AI recently. Bu…
Foundation Models for Autonomous Driving System: An Initial Roadmap
Xiongfei Wu, Mingfei Cheng, Xiaoning Ren +8
Recent advances in foundation models (FMs), including large language models (LLMs), vision-language models (VLMs), and world models, have opened new opportunities for autonomous dr…
Fine-grained Testing for Autonomous Driving Software: a Study on Autoware with LLM-driven Unit Testing
Wenhan Wang, Xuan Xie, Yuheng Huang +3
Testing autonomous driving systems (ADS) is critical to ensuring their reliability and safety. Existing ADS testing works focuses on designing scenarios to evaluate system-level be…