Online Safety Analysis for LLMs: a Benchmark, an Assessment, and a Path Forward

Abstract: While LLMs have seen widespread applications across numerous fields, their limited interpretability poses concerns regarding their safe operations from multiple aspects, e.g., truthfulness, robustness, and fairness. Recent research has started developing quality assurance methods for LLMs, introducing techniques such as offline detector-based or uncertainty estimation methods. However, these approaches predominantly concentrate on post-generation analysis, leaving the online safety analysis for LLMs during the generation phase an unexplored area. To bridge this gap, we conduct in this work a comprehensive evaluation of the effectiveness of existing online safety analysis methods on LLMs. We begin with a pilot study that validates the feasibility of detecting unsafe outputs in the early generation process. Following this, we establish the first publicly available benchmark of online safety analysis for LLMs, including a broad spectrum of methods, models, tasks, datasets, and evaluation metrics. Utilizing this benchmark, we extensively analyze the performance of state-of-the-art online safety analysis methods on both open-source and closed-source LLMs. This analysis reveals the strengths and weaknesses of individual methods and offers valuable insights into selecting the most appropriate method based on specific application scenarios and task requirements. Furthermore, we also explore the potential of using hybridization methods, i.e., combining multiple methods to derive a collective safety conclusion, to enhance the efficacy of online safety analysis for LLMs. Our findings indicate a promising direction for the development of innovative and trustworthy quality assurance methodologies for LLMs, facilitating their reliable deployments across diverse domains.

• The paper introduces a novel benchmark for real-time LLM safety analysis by evaluating eight distinct methods.
• It empirically demonstrates that early detection of unsafe outputs can optimize computational resources and enable timely interventions.
• Hybridization strategies are explored, suggesting adaptive safety techniques for both open and closed-source LLMs.

Online Safety Analysis for LLMs: A Comprehensive Evaluation

The paper, "Online Safety Analysis for LLMs: a Benchmark, an Assessment, and a Path Forward" (2404.08517), provides a thorough investigation into the challenges and methodologies associated with ensuring the safe operation of LLMs. Recognizing the growing deployment of LLMs in various sectors, the study emphasizes the necessity of safety analysis during the online, or real-time, execution phase. This work presents a novel benchmark for evaluating online safety methodologies, analyzes the effectiveness of different safety strategies, and explores the potential for hybrid solutions.

Introduction and Motivation

The proliferation of LLMs in fields ranging from healthcare to finance highlights their expansive capabilities but also underscores substantial safety concerns like hallucinations, toxicity, and bias. While recent efforts have focused on post-generation analysis through techniques such as detector-based methods and uncertainty estimations, there exists a gap in real-time, or online, safety analysis during the generation phase.

This study sets out to fill this gap by examining the feasibility of early-stage detection of unsafe outputs, establishing a benchmark for online safety analysis, and evaluating the effectiveness of existing methodologies across various LLMs and tasks. The ultimate goal is to identify promising directions for future safety assurance technologies.

Benchmark Construction

The paper introduces a diverse and comprehensive benchmark including:

• Online Safety Analysis Methods: Eight methods spanning black-box, white-box, and grey-box categories.
• LLMs: A thorough examination of both open-source models (e.g., LLaMA, Vicuna) and closed-source models (e.g., GPT-3, GPT-4).
• Tasks: Diverse applications such as question answering, text continuation, machine translation, and code generation.
• Metrics: Evaluations based on Safety Gain (SG), Residual Hazard (RH), Availability Cost (AC), and traditional metrics like AUC and time cost.

Pilot Study and Results

The pilot study aimed to verify the feasibility of identifying unsafe outputs during the early stages of LLM generation. Utilizing datasets such as TruthfulQA and RealToxicityPrompt, the study demonstrated that a significant portion of unsafe outputs could be detected early, thereby supporting the importance of integrating online safety analysis into LLMs.

Figure 1: Pilot Study Result of TruthfulQA, result in %.

This early detection capability not only promises to optimize computational resources but also ensures timely intervention to mitigate potential harms.

Empirical Evaluation of Online Safety Analysis Methods

The paper proceeds with an extensive empirical evaluation of the collected safety analysis methods across both open and closed-source LLMs.

• Open-Source Models: Box-based methods demonstrated superior safety gains in NLP tasks, while grey-box methods balanced performance and efficiency across both NLP and coding tasks.
• Closed-Source Models: Entropy-based analyses excelled in achieving high safety gains, especially in text-related tasks, while likelihood-based methods showed strengths in detecting untruthfulness and minimizing availability costs.

  Figure 2: RQ3 - Radar plots of the performance of online safety analysis methods on closed-source LLMs. (SG: Safety Gain; RH: Residual Hazard; AC Availability Cost; Time in seconds.)

Hybridization Approaches

The paper explores hybridization strategies to leverage the strengths of individual analysis methods. While hybrid methods showed potential in improving performance across multiple metrics, consistency remains a challenge. The research suggests future directions in developing sophisticated hybrid methodologies that can dynamically adapt based on task and model-specific characteristics.

Conclusion

The study presents a landmark examination of online safety analysis for LLMs, offering a robust benchmark and critical insights into the effectiveness of existing methods. This research not only highlights the importance of real-time safety assurance but also lays the foundation for developing innovative hybrid approaches. The findings advocate for ongoing exploration into LLM-specific safety strategies, emphasizing the adaptation and integration of safety measures into the deployment of LLMs across diverse domains.