Summary of Llmguard: Guarding Against Unsafe Llm Behavior, by Shubh Goyal et al.
LLMGuard: Guarding Against Unsafe LLM Behavior
by Shubh Goyal, Medha Hira, Shubham Mishra, Sukriti Goyal, Arnav Goel, Niharika Dadu, Kirushikesh DB, Sameep Mehta, Nishtha Madaan
First submitted to arxiv on: 27 Feb 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Cryptography and Security (cs.CR); 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 The paper introduces “LLMGuard”, a tool designed to monitor and flag inappropriate, biased, or misleading content generated by Large Language Models (LLMs) in enterprise settings. This is crucial as LLMs can produce content that violates regulations, causing legal concerns. To achieve this, LLMGuard employs an ensemble of detectors, ensuring robust monitoring of user interactions with LLM applications. The tool aims to prevent the generation of harmful or offensive content, thereby promoting a safer and more regulated use of LLMs in professional settings. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper presents a new tool called “LLMGuard” that helps keep Large Language Models (LLMs) from creating bad content. When people interact with an LLM app, it can generate things that are inappropriate or misleading. This tool monitors those interactions and flags the problem if something goes wrong. It’s like having a safety net to catch any mistakes before they become a big deal. The goal is to make sure LLMs are used responsibly and don’t create any trouble. |
