Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

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

Ximing Dong, Dayi Lin, Shaowei Wang, Ahmed E. Hassan

TL;DR

This paper addresses the risk of harmful outputs from large language models by introducing LLMSafeGuard, a lightweight real-time safeguarding framework that integrates a similarity-based external validator into decoding and employs a context-wise timing strategy to selectively validate candidate tokens. Unlike prior methods that require training external discriminators and tightly coupled control models, LLMSafeGuard relies on demonstration examples to define unsafe content and uses cosine similarity to filter candidates, enabling easy addition of new constraints. Empirical results on detoxification and copyright tasks show substantial reductions in toxicity (at least 38.6%) and improved safety-efficiency trade-offs, including a reduction in inference time by up to 24.2%, while preserving linguistic quality. The work demonstrates a practical, flexible approach to real-time text safeguarding with tunable parameters and open resources for further research and deployment.

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.

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

TL;DR

This paper addresses the risk of harmful outputs from large language models by introducing LLMSafeGuard, a lightweight real-time safeguarding framework that integrates a similarity-based external validator into decoding and employs a context-wise timing strategy to selectively validate candidate tokens. Unlike prior methods that require training external discriminators and tightly coupled control models, LLMSafeGuard relies on demonstration examples to define unsafe content and uses cosine similarity to filter candidates, enabling easy addition of new constraints. Empirical results on detoxification and copyright tasks show substantial reductions in toxicity (at least 38.6%) and improved safety-efficiency trade-offs, including a reduction in inference time by up to 24.2%, while preserving linguistic quality. The work demonstrates a practical, flexible approach to real-time text safeguarding with tunable parameters and open resources for further research and deployment.

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.
Paper Structure (26 sections, 2 equations, 4 figures, 8 tables, 3 algorithms)

This paper contains 26 sections, 2 equations, 4 figures, 8 tables, 3 algorithms.

Table of Contents

  1. Introduction
  2. Background & Related work
  3. Large language model
  4. Safeguarding large language models
  5. Methodology
  6. Similarity-based external validator
  7. Context-wise timing selection
  8. Experimental Setting
  9. Tasks
  10. Detoxification
  11. Copyright
  12. Base large language models and sampling techniques
  13. Results
  14. Effectiveness of LLMSafeGuard
  15. Ablation analysis of Context-wise
  16. ...and 11 more sections

Figures (4)

  • Figure 1: The workflow of LLMSafeGuard involves safeguarding text generation by using an external validator during the decoding stage. Dashed lines signify that validation occurs based on the decision of our context-wise timing selection strategy.
  • Figure 2: The proportion of invalid candidates against each time step (a) and the boxplot of similarity between candidates and demonstration examples over each time step (b).
  • Figure 3: The impact of $ThrV$ on the performance of LLMSafeGuard across Toxic and Copyright datasets.
  • Figure 4: The impact of $\lambda$ on the performance of LLMSafeGuard across Toxic and Copyright datasets.