Decision Models for Guardrails: Exploring Jev and Kev for Moderation
A closer look at the new paradigm of frontier models, and how we explored Jev and adapted its open-source interpretation - Kev, for Singapore-context moderation.
Leanne Tan
Data Scientist at GovTech's AI Practice (Responsible AI). Learning new things every day, here to share stories and insights! Let's connect :)
Behind VEILED Expressions: A Singapore-context benchmark for mental-health AI evaluation
We built VEILED-SH to evaluate indirect self-harm/suicide language in the Singapore context, shaped by local language, privacy safeguards and practitioner insight.
Building an automated Evals workflow that works (and open-sourcing it)
How we built Kaleidoscope: A structured workflow for realistic, scalable, and human-aligned contextual AI evaluations.
Yes, you’re absolutely right… Right? A mini survey on LLM sycophancy
Ever spoken to an AI and felt like it was responding with insincere praise?
MetaEvaluator: Systematically Evaluate Your LLM Judges
Measure how well your app is performing and more importantly where it's failing.
Benchmarking GPT-5 & GPT-OSS: A Responsible AI Approach
Evaluating dimensions often overlooked by traditional benchmarks.
Introducing LionGuard 2: Multilingual LLM Guardrail for Singapore
We improved its coverage and robustness.
RabakBench: Multilingual AI Safety Evaluation Made Local
Global safety guardrails are often blind to local dialects and sensitivities.
Validating Annotation Agreement between Humans and LLMs
Who Judges the Judge?
At GovTech’s AI Practice, we’ve been embracing what’s known as “LLM-as-a-judge” — essentially employing LLMs as evaluators across our AI workflows. This approach has become one powerful approach in our evaluation toolkit.
We use LLMs extensively across multiple areas: judging other