Hidden Reliability Risks in Large Language Models: Systematic Identification of Precision-Induced Output Disagreements
Summary: arXiv:2604.19790v1 Announce Type: new
Abstract: Large language models (LLMs) are increasingly deployed under diverse numerical precision configurations, including standard floating-point formats (e.g., bfloat16 and float16) and quantized integer formats (e.g., int16 and int8), to meet efficiency and resource constraints. However, minor inconsistencies between LLMs of different precisions are difficult to detect and are often overlooked by existing evaluation methods. In this paper, we present PrecisionDiff, an automated differential testing framework for systematically detecting precision-induced behavioral disagreements in LLMs.
PrecisionDiff generates precision-sensitive test inputs and performs cross-precision comparative analysis to uncover subtle divergences that remain hidden under conventional testing strategies. To demonstrate its practical significance, we instantiate PrecisionDiff on the alignment verification task, where precision-induced disagreements manifest as jailbreak divergence-inputs that are rejected under one precision may produce harmful responses under another.
Key Findings
Experimental results show that such behavioral disagreements are widespread across multiple open-source aligned LLMs and precision settings, and that PrecisionDiff significantly outperforms vanilla testing methods in detecting these issues. Our work enables automated precision-sensitive test generation, facilitating effective pre-deployment evaluation and improving precision robustness during training.
Introduction
The deployment of large language models has become a cornerstone of artificial intelligence, with applications ranging from natural language processing to conversational agents. As these models grow in complexity, the choice of numerical precision for model parameters becomes critical. Different precision settings can lead to variations in model behavior, which may not be immediately apparent.
Challenges of Precision Variability
Traditional testing methods often focus on overall performance metrics, missing out on detecting subtle discrepancies that arise from differences in numerical precision. As a result, models may exhibit unexpected behaviors when subjected to inputs processed under varying precision formats.
PrecisionDiff: An Innovative Solution
PrecisionDiff addresses these challenges by:
- Generating test inputs: It creates precision-sensitive test cases that are specifically designed to highlight discrepancies in model output.
- Cross-precision analysis: The framework facilitates a comparative analysis between different precision formats, allowing for a thorough evaluation of behavioral consistency.
- Enhancing reliability: By identifying problematic areas in model outputs, PrecisionDiff contributes to building more reliable and robust LLMs.
Conclusion
The research underscores the importance of precision in the evaluation and deployment of large language models. With the introduction of PrecisionDiff, developers and researchers can better understand and mitigate the risks associated with precision-induced output disagreements. This advancement not only enhances model reliability but also promotes the responsible deployment of AI technologies.