Publications (32)
Can I use this publicly available dataset to build commercial AI software? -- A Case Study on Publicly Available Image Datasets
Gopi Krishnan Rajbahadur, Erika Tuck, Li Zi +5
Publicly available datasets are one of the key drivers for commercial AI software. The use of publicly available datasets is governed by dataset licenses. These dataset licenses ou…
A Framework for Real-time Safeguarding the Text Generation of Large Language Model
Ximing Dong, Dayi Lin, Shaowei Wang +1
Large Language Models (LLMs) have significantly advanced natural language processing (NLP) tasks but also pose ethical and societal risks due to their propensity to generate harmfu…
Rethinking Software Engineering in the Foundation Model Era: A Curated Catalogue of Challenges in the Development of Trustworthy FMware
Ahmed E. Hassan, Dayi Lin, Gopi Krishnan Rajbahadur +9
Foundation models (FMs), such as Large Language Models (LLMs), have revolutionized software development by enabling new use cases and business models. We refer to software built us…
Watson: A Cognitive Observability Framework for the Reasoning of LLM-Powered Agents
Benjamin Rombaut, Sogol Masoumzadeh, Kirill Vasilevski +2
Large language models (LLMs) are increasingly integrated into autonomous systems, giving rise to a new class of software known as Agentware, where LLM-powered agents perform comple…
SWE-Effi: Re-Evaluating Software AI Agent System Effectiveness Under Resource Constraints
Zhiyu Fan, Kirill Vasilevski, Dayi Lin +6
The advancement of large language models (LLMs) and code agents has demonstrated significant potential to assist software engineering (SWE) tasks, such as autonomous issue resoluti…
Real-time Adapting Routing (RAR): Improving Efficiency Through Continuous Learning in Software Powered by Layered Foundation Models
Kirill Vasilevski, Dayi Lin, Ahmed E. Hassan
To balance the quality and inference cost of a Foundation Model (FM, such as large language models (LLMs)) powered software, people often opt to train a routing model that routes r…