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Gabriel C

gabrielchua
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https://gabrielchua.me

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updated a Space 1 day ago
govtech/off-topic-demo
posted an update 2 days ago
Sharing my first paper! == Large Language Models (LLMs) are powerful, but they're prone to off-topic misuse, where users push them beyond their intended scope. Think harmful prompts, jailbreaks, and misuse. So how do we build better guardrails? Traditional guardrails rely on curated examples or classifiers. The problem? ⚠️ High false-positive rates ⚠️ Poor adaptability to new misuse types ⚠️ Require real-world data, which is often unavailable during pre-production Our method skips the need for real-world misuse examples. Instead, we: 1️⃣ Define the problem space qualitatively 2️⃣ Use an LLM to generate synthetic misuse prompts 3️⃣ Train and test guardrails on this dataset We apply this to the off-topic prompt detection problem, and fine-tune simple bi- and cross-encoder classifiers that outperform heuristics based on cosine similarity or prompt engineering. Additionally, framing the problem as prompt relevance allows these fine-tuned classifiers to generalise to other risk categories (e.g., jailbreak, toxic prompts). Through this work, we also open-source our dataset (2M examples, ~50M+ tokens) and models. paper: https://huggingface.co/papers/2411.12946 artifacts: https://huggingface.co/collections/govtech/off-topic-guardrail-673838a62e4c661f248e81a4
upvoted a paper 2 days ago
Harnessing the Potential of Gen-AI Coding Assistants in Public Sector Software Development
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