mlsanityData to 7 Oct 2026

LLMGuard: Guarding Against Unsafe LLM Behavior

arXiv 2403.00826 ↗First posted 27 Feb 2024cs.CL cs.CR cs.LGcited by 10

Not on the map. Why?

Abstract (arXiv)

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.

Key facts. Cited by 10 papers in the map (data through 7 Oct 2026). Builds on 7 of its 17 references that are in the map. First posted on arXiv 27 Feb 2024.

Builds on

7 of 17 references.

References · 7

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  2. 2023Llama 2: Open Foundation and Fine-Tuned Chat Modelscited by 12,865
  3. 2016Ex Machina: Personal Attacks Seen at Scalecited by 208
  4. 2021AMMUS : A Survey of Transformer-based Pretrained Models in Natural Language Processingcited by 131
  5. 2023The MiniPile Challenge for Data-Efficient Language Modelscited by 74
  6. 2021MathBERT: A Pre-Trained Model for Mathematical Formula Understandingcited by 58
  7. 2023Advancements in Scientific Controllable Text Generation Methodscited by 7

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 3; 2025: 6; 2026: 1.

10 papers.