Specification Overfitting in Artificial Intelligence

2024-03-13

Artificial Intelligence Review

AI Summary

This work addresses “specification overfitting” in AI systems driven by formal specifications—where models syntactically satisfy specifications but fail semantically, leading to poor generalization. We formally define and empirically validate this phenomenon for the first time, introducing a diagnostic framework and benchmark suite that explicitly distinguish syntactic compliance from semantic consistency. Our methodology integrates formal verification, adversarial specification generation, behavioral consistency assessment, and large language model–based reasoning analysis. Across multi-task AI verification experiments, we find that 68% of specification-compliant models exhibit semantic failure. Our proposed mitigation strategies improve generalization accuracy by 23.5%. This work establishes both theoretical foundations and practical tools for specification-driven development of trustworthy AI systems.

Technology Category

• Philosophy and Ethics of AI: Safety, Robustness & Trustworthiness
• Constraint Satisfaction and Optimization: Satisfiability Modulo Theories
• Humans and AI: Other Foundations of Human Computation & AI

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems

Problem

• Artificial Intelligence
• Over-optimization
• Objective Detriment

Innovation

• AI specification overfitting
• cross-domain standards review
• nuanced standard implementation