A Framework for Real-time Safeguarding the Text Generation of Large Language Model

2024-04-29

arXiv.org

AI Summary

To address the ethical risks posed by harmful content generation from large language models (LLMs), existing safety interventions rely on auxiliary control models or runtime modifications, often degrading output quality and increasing inference overhead. This paper proposes LLMSafeGuard—a lightweight, fine-tuning-free real-time safety framework that dynamically integrates external verifiers during decoding for immediate safety intervention. Its key contributions are: (1) a similarity-driven, training-free verification mechanism that eliminates the need for model retraining; and (2) a context-aware intervention timing strategy that balances safety guarantees with generation fluency. Experiments demonstrate that LLMSafeGuard reduces toxic output by ≥38.6% in detoxification tasks while preserving language quality, and achieves ≥24.2% lower inference latency compared to state-of-the-art methods.

Technology Category

• Natural Language Processing: Safety and Robustness
• Machine Learning: Large Multimodal Models (LMMs)
• Computer Vision: Large Vision Models

Application Category

• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Economics, Online Markets and Human Computation: LLM based quality controls for crowd work
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

Large Language Models (LLMs) have significantly advanced natural language processing (NLP) tasks but also pose ethical and societal risks due to their propensity to generate harmful content. Existing methods have limitations, including the need for training specific control models and proactive intervention during text generation, that lead to quality degradation and increased computational overhead. To mitigate those limitations, we propose LLMSafeGuard, a lightweight real-time framework that integrates an external validator into decoding, rejecting unsafe outputs while allowing valid ones. We introduce a similarity-based validation approach, simplifying constraint introduction and eliminating the need for control model training. Additionally, LLMSafeGuard employs a context-wise timing selection strategy, intervening LLMs only when necessary. We evaluate LLMSafeGuard on detoxification and copyright safeguarding, demonstrating its superiority over SOTA baselines. In detoxification, LLMSafeGuard reduces toxic output by at least 38.6% while preserving linguistic quality. Additionally, its context-wise timing selection cuts inference time by at least 24.2% without compromising effectiveness.

Problem

• Preventing harmful content generation by LLMs
• Reducing computational overhead in safety frameworks
• Maintaining text quality during real-time safeguarding

Innovation

• Lightweight real-time framework integrating external validator
• Similarity-based validation without control model training
• Context-wise timing selection reducing inference time