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LLMGuard: Guarding Against Unsafe LLM Behavior (2403.00826v1)

Published 27 Feb 2024 in cs.CL, cs.CR, and cs.LG

Abstract: Although the rise of 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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References (25)
  1. Language Models are Few-Shot Learners. arXiv:2005.14165.
  2. PaLM: Scaling Language Modeling with Pathways. arXiv:2204.02311.
  3. Scaling Instruction-Finetuned Language Models. arXiv:2210.11416.
  4. Toxic Comment Classification Challenge. https://kaggle.com/competitions/jigsaw-toxic-comment-classification-challenge. Accessed: 2023-12-12.
  5. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), 4171–4186. Minneapolis, Minnesota: Association for Computational Linguistics.
  6. ChatGPT for (finance) research: The Bananarama conjecture. Finance Research Letters, 53: 103662.
  7. Twitter sentiment classification using distant supervision. CS224N project report, Stanford, 1(12): 2009.
  8. Advancements in Scientific Controllable Text Generation Methods. arXiv:2307.05538.
  9. Hanu, L.; and Unitary team. 2020. Detoxify. https://github.com/unitaryai/detoxify. Accessed: 2023-12-12.
  10. Long short-term memory. Neural computation, 9(8): 1735–1780.
  11. Kaddour, J. 2023. The MiniPile Challenge for Data-Efficient Language Models. arXiv preprint arXiv:2304.08442.
  12. Challenges and applications of large language models. arXiv preprint arXiv:2307.10169.
  13. AMMUS : A Survey of Transformer-based Pretrained Models in Natural Language Processing. arXiv:2108.05542.
  14. Kitamura, F. C. 2023. ChatGPT is shaping the future of medical writing but still requires human judgment.
  15. Mitchell, T. 1999. Twenty Newsgroups. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5C323.
  16. A comparative analysis of machine learning techniques for cyberbullying detection on twitter. Future Internet, 12(11): 187.
  17. OpenAI. 2023. GPT-4 Technical Report. arXiv:2303.08774.
  18. Mathbert: A pre-trained model for mathematical formula understanding. arXiv preprint arXiv:2105.00377.
  19. Red teaming language models with language models. arXiv preprint arXiv:2202.03286.
  20. Language models are unsupervised multitask learners. OpenAI blog, 1(8): 9.
  21. Llama 2: Open foundation and fine-tuned chat models. arXiv preprint arXiv:2307.09288.
  22. The Silence of the LLMs: Cross-Lingual Analysis of Political Bias and False Information Prevalence in ChatGPT, Google Bard, and Bing Chat.
  23. FairPy: A Toolkit for Evaluation of Social Biases and their Mitigation in Large Language Models. arXiv preprint arXiv:2302.05508.
  24. Ex Machina: Personal Attacks Seen at Scale. In Proceedings of the 26th International Conference on World Wide Web, WWW ’17, 1391–1399. Republic and Canton of Geneva, CHE: International World Wide Web Conferences Steering Committee. ISBN 9781450349130.
  25. A survey of large language models. arXiv preprint arXiv:2303.18223.
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Authors (9)
  1. Shubh Goyal (1 paper)
  2. Medha Hira (5 papers)
  3. Shubham Mishra (8 papers)
  4. Sukriti Goyal (1 paper)
  5. Arnav Goel (6 papers)
  6. Niharika Dadu (3 papers)
  7. Kirushikesh DB (3 papers)
  8. Sameep Mehta (27 papers)
  9. Nishtha Madaan (12 papers)
Citations (5)
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Summary

Unveiling LLMGuard: A Comprehensive Framework for Mitigating Unsafe Behaviors in LLMs

Introduction

The proliferation of LLMs across diverse sectors underscores their transformative potential in automating complex NLP tasks. However, this rapid adoption surfaces critical challenges linked to the generation of inappropriate, biased, or misleading content, posing significant risks in regulatory compliance and ethical conduct. Addressing these concerns, a novel framework, LLMGuard, emerges, offering a robust solution by monitoring user interactions with LLM applications through an ensemble of detectors designed to flag and mitigate unsafe behaviors.

Core Contribution

LLMGuard delineates a significant advancement in safeguarding against undesirable LLM outputs by integrating a comprehensive suite of detectors. Each detector within this ensemble specializes in identifying specific forms of unsafe content, including biases, personal identifiable information (PII), toxicity, violence, and blacklisted topics. This modular structure enhances adaptability, enabling seamless updates or modifications to the detector library, thus maintaining relevance amidst evolving content safety standards. The tool's architecture incorporates independent operation of detectors, ensuring a nuanced approach to content monitoring without compromising the efficiency and scalability of LLM applications.

Specific Detectors and Their Efficacy

  • Racial Bias Detector: Implements LSTM architecture and demonstrates an 87.2% accuracy in identifying racially prejudiced content, paving the way for more equitable LLM interactions.
  • Violence Detector: Utilizes an MLP with a simple count-based mapping for text vectorization, achieving an 86.4% accuracy rate in flagging texts with violent or threatening undertones.
  • Blacklisted Topics Detector: Employs fine-tuned BERT models to discern sensitive topics defined by users, boasting an approximate 92% accuracy across politics, religion, and sports categories.
  • PII Detector: Applies regular expressions for the detection of sensitive personal data, safeguarding user privacy with an NER F1-score of 85%.
  • Toxicity Detector: Leverages the Detoxify model to identify various forms of toxic content, achieving a commendable mean AUC score of 98.64% in toxicity classification.

Practical Implications and Theoretical Contributions

The introduction of LLMGuard represents a pragmatic approach to enhancing the safety and reliability of LLM applications in real-world settings. By outlaying a systematic framework for the real-time assessment and filtering of LLM-generated content, it mitigates the risks associated with data privacy, ethical breaches, and regulatory non-compliance. Theoretically, LLMGuard extends the discourse on content safety in AI, providing empirical evidence on the effectiveness of ensemble methods in addressing complex challenges inherent to LLM outputs. Further, it accentuates the importance of adaptability and modularity in designing AI safety tools, offering insights that could inspire future research in AI ethics and safety protocols.

Demo Insights and Framework Usability

The practical effectiveness of LLMGuard is showcased through a comprehensive demo, integrating the tool with FLAN-T5 and GPT-2 models to illustrate its real-world applicability. This user-centric demonstration emphasizes the tool’s flexibility, allowing for customized activation of specific detectors based on user needs and the seamless integration of guardrails within existing LLM infrastructures.

Future Perspectives

Looking ahead, the evolution of LLMGuard anticipates the continuous expansion and refinement of its detector library to encompass a broader spectrum of unsafe behaviors and content types. This entails leveraging advancements in AI and machine learning to enhance the precision and recall of existing detectors, as well as exploring innovative approaches to safeguarding against emerging challenges in content safety. Furthermore, expanding the tool's compatibility with a wider array of LLM architectures could amplify its impact, establishing LLMGuard as a cornerstone in the ethical deployment of LLMs across industries.

In conclusion, LLMGuard emerges as a pivotal framework in the field of AI safety, offering a scalable and effective solution to the multifaceted challenges of mitigating unsafe behaviors in LLM applications. Its contribution not only advances our understanding of content safety mechanisms but also sets a new benchmark for responsible AI development and deployment amidst the rapidly evolving landscape of machine learning technologies.