Bridging the Safety Gap: A Guardrail Pipeline for Trustworthy LLM Inferences
This work addresses safety risks for users of large language models, though it is incremental as it builds on existing methods for detection and correction.
The paper tackles the problem of enhancing safety and reliability in LLM inferences by introducing Wildflare GuardRail, a pipeline that integrates modules for detection, grounding, customization, and repair, achieving results such as 100% accuracy in addressing malicious URLs in 1.06s per query and 80.7% accuracy in reducing hallucinations.
We present Wildflare GuardRail, a guardrail pipeline designed to enhance the safety and reliability of Large Language Model (LLM) inferences by systematically addressing risks across the entire processing workflow. Wildflare GuardRail integrates several core functional modules, including Safety Detector that identifies unsafe inputs and detects hallucinations in model outputs while generating root-cause explanations, Grounding that contextualizes user queries with information retrieved from vector databases, Customizer that adjusts outputs in real time using lightweight, rule-based wrappers, and Repairer that corrects erroneous LLM outputs using hallucination explanations provided by Safety Detector. Results show that our unsafe content detection model in Safety Detector achieves comparable performance with OpenAI API, though trained on a small dataset constructed with several public datasets. Meanwhile, the lightweight wrappers can address malicious URLs in model outputs in 1.06s per query with 100% accuracy without costly model calls. Moreover, the hallucination fixing model demonstrates effectiveness in reducing hallucinations with an accuracy of 80.7%.