LLMGuard: Guarding Against Unsafe LLM Behavior

Shubh Goyal, Medha Hira, Shubham Mishra, Sukriti Goyal, Arnav Goel +4 more
2/27/2024
cs.CLcs.CRcs.LG

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Abstract

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.

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Cite this paper

@article{goyal2024llmguard,
  title  = {LLMGuard: Guarding Against Unsafe LLM Behavior},
  author = {Shubh Goyal and Medha Hira and Shubham Mishra and Sukriti Goyal and Arnav Goel and Niharika Dadu and Kirushikesh DB and Sameep Mehta and Nishtha Madaan},
  year   = {2024},
  eprint = {2403.00826},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2403.00826v1}
}

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