Summary of A Flexible Large Language Models Guardrail Development Methodology Applied to Off-topic Prompt Detection, by Gabriel Chua et al.
A Flexible Large Language Models Guardrail Development Methodology Applied to Off-Topic Prompt Detection
by Gabriel Chua, Shing Yee Chan, Shaun Khoo
First submitted to arxiv on: 20 Nov 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel methodology is proposed to develop flexible and data-free guardrails for large language models (LLMs) that prevent off-topic misuse. The approach involves defining the problem space qualitatively, generating diverse prompts through an LLM, and constructing a synthetic dataset to benchmark and train guardrails. The resulting guardrails outperform heuristic approaches and generalize well to other misuse categories, including jailbreak and harmful prompts. To facilitate further research and development, the authors open-source the synthetic dataset and off-topic guardrail models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large Language Models (LLMs) can be misused by users who prompt them to do things they weren’t meant to do. Right now, we don’t have a good way to stop this from happening. Current methods are limited because they rely on special examples or custom tools that aren’t very effective. In this paper, we introduce a new way to develop guardrails for LLMs that doesn’t require any data. We define what the problem is and then use an LLM to come up with different prompts. This helps us create a dataset to test our guardrails and train them to work well. Our guardrails are better than what’s currently available and can even handle other types of misuse. To help others, we’re making our dataset and guardrail models freely available. |
Keywords
* Artificial intelligence * Prompt
