Robert Farrell, Rajarshi Das, et al.
AAAI-SS 2010
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.
Robert Farrell, Rajarshi Das, et al.
AAAI-SS 2010
Chen-chia Chang, Wan-hsuan Lin, et al.
ICML 2025
Daniel Karl I. Weidele, Hendrik Strobelt, et al.
SysML 2019
Gang Liu, Michael Sun, et al.
ICLR 2025