Evaluating Reliability Gaps in Large Language Model Safety via Repeated Prompt Sampling
Summary: arXiv:2604.09606v1 Announce Type: new
Abstract: Traditional benchmarks for large language models (LLMs), such as HELM and AIR-BENCH, primarily assess safety risk through breadth-oriented evaluation across diverse tasks. However, real-world deployment often exposes a different class of risk: operational failures arising from repeated generations of the same prompt rather than broad task generalization. In high-stakes settings, response consistency and safety under repeated use are critical operational requirements.
Introduction
In the evolving landscape of artificial intelligence, ensuring the reliability and safety of large language models (LLMs) has become a focal point for researchers and developers. The introduction of Accelerated Prompt Stress Testing (APST) marks a significant advancement in evaluating these models, particularly in contexts where repeated prompt generation is common. This article explores the framework of APST, its methodologies, and implications for model deployment in high-stakes environments.
Understanding Accelerated Prompt Stress Testing (APST)
APST serves as a depth-oriented evaluation framework, drawing inspiration from reliability engineering’s stress-testing techniques. The primary objective of APST is to uncover latent failure modes that may not be apparent through conventional evaluation methods. Key features of APST include:
- Controlled Operational Conditions: The framework allows for systematic testing of LLMs under varying conditions, such as temperature variations and prompt perturbations.
- Repeated Prompt Sampling: By repeatedly sampling identical prompts, APST aims to reveal inconsistencies and failures that arise from operational contexts.
- Statistical Characterization of Failures: Instead of viewing failures as isolated incidents, APST characterizes them as stochastic outcomes, allowing for a statistical analysis of operational risks.
Modeling Safety Failures
One of the innovative aspects of APST is its approach to modeling safety failures. The framework utilizes Bernoulli and binomial formulations to estimate per-inference failure probabilities. This statistical modeling enables researchers to:
- Quantitatively compare operational risks across different models and configurations.
- Identify specific failure modes, such as hallucinations, inconsistency in refusals, and unsafe completions.
- Provide insights into how LLMs behave under repeated use, which is crucial for applications in high-stakes scenarios.
Application and Findings
APST was applied to multiple instruction-tuned LLMs evaluated on AIR-BENCH 2024 derived safety and security prompts. Initial findings indicate that while models perform similarly under traditional evaluation settings, the APST reveals significant discrepancies in response consistency and safety during repeated prompt generations. This highlights the importance of adopting depth-oriented evaluations in addition to traditional benchmark assessments.
Conclusion
As the deployment of large language models becomes more prevalent in critical applications, ensuring their reliability and safety is paramount. The Accelerated Prompt Stress Testing framework provides a robust methodology for uncovering reliability gaps that traditional evaluation methods may overlook. By focusing on the operational risks associated with repeated prompt sampling, APST sets a new standard for assessing the performance and safety of LLMs in real-world contexts.