Post
1197
Sharing my first paper!
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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?
â ď¸ High false-positive rates
â ď¸ Poor adaptability to new misuse types
â ď¸ Require real-world data, which is often unavailable during pre-production
Our method skips the need for real-world misuse examples. Instead, we:
1ď¸âŁ Define the problem space qualitatively
2ď¸âŁ Use an LLM to generate synthetic misuse prompts
3ď¸âŁ 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: A Flexible Large Language Models Guardrail Development Methodology Applied to Off-Topic Prompt Detection (2411.12946)
artifacts: govtech/off-topic-guardrail-673838a62e4c661f248e81a4
==
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?
â ď¸ High false-positive rates
â ď¸ Poor adaptability to new misuse types
â ď¸ Require real-world data, which is often unavailable during pre-production
Our method skips the need for real-world misuse examples. Instead, we:
1ď¸âŁ Define the problem space qualitatively
2ď¸âŁ Use an LLM to generate synthetic misuse prompts
3ď¸âŁ 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: A Flexible Large Language Models Guardrail Development Methodology Applied to Off-Topic Prompt Detection (2411.12946)
artifacts: govtech/off-topic-guardrail-673838a62e4c661f248e81a4
