5 papers
Understanding, Detecting, and Repairing Real-World In-Context-Learning-Based Text-to-SQL Errors
Jiawei Shen, Chengcheng Wan, Ruoyi Qiao +6
Large language models (LLMs) have been adopted for text-to-SQL tasks, utilizing their in-context learning (ICL) capability to translate natural language questions into SQL queries.…
Assessing the Impact of Requirement Ambiguity on LLM-based Function-Level Code Generation
Di Yang, Xinou Xie, Xiuwen Yang +7
Software requirement ambiguity is ubiquitous in real-world development, stemming from the inherent imprecision of natural language and the varying interpretations of stakeholders.…
Improving Random Testing via LLM-powered UI Tarpit Escaping for Mobile Apps
Mengqian Xu, Yiheng Xiong, Le Chang +3
Random GUI testing is a widely-used technique for testing mobile apps. However, its effectiveness is limited by the notorious issue -- UI exploration tarpits, where the exploration…
Scale-Invariant Adversarial Attack against Arbitrary-scale Super-resolution
Yihao Huang, Xin Luo, Qing Guo +5
The advent of local continuous image function (LIIF) has garnered significant attention for arbitrary-scale super-resolution (SR) techniques. However, while the vulnerabilities of…
Perception-guided Jailbreak against Text-to-Image Models
Yihao Huang, Le Liang, Tianlin Li +5
In recent years, Text-to-Image (T2I) models have garnered significant attention due to their remarkable advancements. However, security concerns have emerged due to their potential…