89 citations · 118 across the 7 of their papers we have counts for
7 papers
Learning to Ask: When LLM Agents Meet Unclear Instruction
Wenxuan Wang, Juluan Shi, Zixuan Ling +7
Equipped with the capability to call functions, modern large language models (LLMs) can leverage external tools for addressing a range of tasks unattainable through language skills…
UniDebugger: Hierarchical Multi-Agent Framework for Unified Software Debugging
Cheryl Lee, Chunqiu Steven Xia, Longji Yang +4
Software debugging is a time-consuming endeavor involving a series of steps, such as fault localization and patch generation, each requiring thorough analysis and a deep understand…
Identifying the Achilles' Heel: An Iterative Method for Dynamically Uncovering Factual Errors in Large Language Models
Wenxuan Wang, Yuk-Kit Chan, Zixuan Ling +7
Large Language Models (LLMs) like ChatGPT are foundational in various applications due to their extensive knowledge from pre-training and fine-tuning. Despite this, they are prone…
What Makes Good In-context Demonstrations for Code Intelligence Tasks with LLMs?
Shuzheng Gao, Xin-Cheng Wen, Cuiyun Gao +3
Pre-trained models of source code have gained widespread popularity in many code intelligence tasks. Recently, with the scaling of the model and corpus size, large language models…
MicroRes: Versatile Resilience Profiling in Microservices via Degradation Dissemination Indexing
Tianyi Yang, Cheryl Lee, Jiacheng Shen +3
Microservice resilience, the ability of microservices to recover from failures and continue providing reliable and responsive services, is crucial for cloud vendors. However, the c…
Memory-Safety Challenge Considered Solved? An In-Depth Study with All Rust CVEs
Hui Xu, Zhuangbin Chen, Mingshen Sun +2
Rust is an emerging programing language that aims at preventing memory-safety bugs without sacrificing much efficiency. The claimed property is very attractive to developers, and m…
Doctor of Crosswise: Reducing Over-parametrization in Neural Networks
J. D. Curtó, I. C. Zarza, Kris Kitani +2
Dr. of Crosswise proposes a new architecture to reduce over-parametrization in Neural Networks. It introduces an operand for rapid computation in the framework of Deep Learning tha…