LLMGuard: Guarding Against Unsafe LLM Behavior
This addresses safety concerns for enterprises using LLMs, but it appears incremental as it builds on existing detection methods.
The authors tackled the problem of unsafe content generation by LLMs in enterprise settings by developing LLMGuard, a tool that monitors interactions and flags inappropriate content using an ensemble of detectors, though no concrete performance numbers are provided.
Although the rise of Large Language Models (LLMs) in enterprise settings brings new opportunities and capabilities, it also brings challenges, such as the risk of generating inappropriate, biased, or misleading content that violates regulations and can have legal concerns. To alleviate this, we present "LLMGuard", a tool that monitors user interactions with an LLM application and flags content against specific behaviours or conversation topics. To do this robustly, LLMGuard employs an ensemble of detectors.