Beyond Accuracy: Introducing a Symbolic-Mechanistic Approach to Interpretable Evaluation

TL;DR

This paper advocates for mechanism-aware evaluation combining symbolic rules with interpretability to better assess model generalization, demonstrated through NL-to-SQL tasks revealing limitations of accuracy metrics.

Contribution

It introduces a symbolic-mechanistic evaluation framework that identifies genuine understanding versus superficial pattern exploitation in models.

Findings

Standard accuracy metrics can be misleading in small-data regimes.

Symbolic-mechanistic evaluation exposes schema generalization failures.

Models can achieve high accuracy yet violate core rules.

Abstract

Accuracy-based evaluation cannot reliably distinguish genuine generalization from shortcuts like memorization, leakage, or brittle heuristics, especially in small-data regimes. In this position paper, we argue for mechanism-aware evaluation that combines task-relevant symbolic rules with mechanistic interpretability, yielding algorithmic pass/fail scores that show exactly where models generalize versus exploit patterns. We demonstrate this on NL-to-SQL by training two identical architectures under different conditions: one without schema information (forcing memorization), one with schema (enabling grounding). Standard evaluation shows the memorization model achieves 94% field-name accuracy on unseen data, falsely suggesting competence. Our symbolic-mechanistic evaluation reveals this model violates core schema generalization rules, a failure invisible to accuracy metrics.