HIP-LLM: A Hierarchical Imprecise Probability Approach to Reliability Assessment of Large Language Models

2025-11-01

AI Summary

Current LLM reliability assessment predominantly relies on static accuracy metrics, failing to characterize probabilistic behavior and epistemic uncertainty under realistic operational conditions. To address this, we propose HIP-LLM: the first hierarchical framework integrating software reliability engineering with imprecise probability theory. HIP-LLM employs hierarchical Bayesian modeling, uncertainty-aware priors, and operational profile integration to enable multi-granularity reliability inference—from subdomains to system-level—and derives posterior reliability envelopes. Its key contribution is the novel incorporation of imprecise probability into LLM reliability modeling, explicitly quantifying epistemic uncertainty. Extensive evaluation across multiple benchmarks demonstrates significant improvements in assessment accuracy and standardization. Furthermore, we release a fully open-source, reproducible implementation to support community validation and extension.

Technology Category

• Reasoning under Uncertainty: Relational Probabilistic Models
• Machine Learning: Calibration & Uncertainty Quantification
• Natural Language Processing: (Large) Language Models

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Search and Retrieval-Augmented AI: Search Tool Learning with LLM: Teaching LLMs to invoke search and make use of retrieved information
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation

Abstract

Large Language Models (LLMs) are increasingly deployed across diverse domains, raising the need for rigorous reliability assessment methods. Existing benchmark-based evaluations primarily offer descriptive statistics of model accuracy over datasets, providing limited insight into the probabilistic behavior of LLMs under real operational conditions. This paper introduces HIP-LLM, a Hierarchical Imprecise Probability framework for modeling and inferring LLM reliability. Building upon the foundations of software reliability engineering, HIP-LLM defines LLM reliability as the probability of failure-free operation over a specified number of future tasks under a given Operational Profile (OP). HIP-LLM represents dependencies across (sub-)domains hierarchically, enabling multi-level inference from subdomain to system-level reliability. HIP-LLM embeds imprecise priors to capture epistemic uncertainty and incorporates OPs to reflect usage contexts. It derives posterior reliability envelopes that quantify uncertainty across priors and data. Experiments on multiple benchmark datasets demonstrate that HIP-LLM offers a more accurate and standardized reliability characterization than existing benchmark and state-of-the-art approaches. A publicly accessible repository of HIP-LLM is provided.

Problem

• Assessing LLM reliability under real operational conditions
• Modeling hierarchical dependencies across domains for reliability
• Quantifying epistemic uncertainty in LLM probabilistic behavior

Innovation

• Hierarchical Imprecise Probability framework for LLM reliability
• Models dependencies across domains with multi-level inference
• Embeds imprecise priors to quantify epistemic uncertainty