3 papers
cs.SE2025
Measuring What Matters: A Framework for Evaluating Safety Risks in Real-World LLM Applications
Jia Yi Goh, Shaun Khoo, Nyx Iskandar +3
Most safety testing efforts for large language models (LLMs) today focus on evaluating foundation models. However, there is a growing need to evaluate safety at the application lev…
cs.CL2025
MinorBench: A hand-built benchmark for content-based risks for children
Shaun Khoo, Gabriel Chua, Rachel Shong
Large Language Models (LLMs) are rapidly entering children's lives - through parent-driven adoption, schools, and peer networks - yet current AI ethics and safety research do not a…
cs.CL2024
A Flexible Large Language Models Guardrail Development Methodology Applied to Off-Topic Prompt Detection
Gabriel Chua, Shing Yee Chan, Shaun Khoo
Large Language Models (LLMs) are prone to off-topic misuse, where users may prompt these models to perform tasks beyond their intended scope. Current guardrails, which often rely o…