Beyond Accuracy: Introducing a Symbolic-Mechanistic Approach to Interpretable Evaluation
This addresses the problem of unreliable model evaluation for researchers and practitioners, particularly in small-data regimes, by offering a more interpretable method, though it is incremental as it builds on existing interpretability and evaluation concepts.
The paper tackles the problem of accuracy-based evaluation failing to distinguish genuine generalization from shortcuts like memorization, proposing a symbolic-mechanistic approach that combines task-relevant symbolic rules with mechanistic interpretability to provide algorithmic pass/fail scores. In a demonstration on NL-to-SQL, it shows that a memorization model achieves 94% field-name accuracy on unseen data, falsely suggesting competence, but the new evaluation reveals it violates core schema generalization rules.
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.