Beyond Accuracy: Introducing a Symbolic-Mechanistic Approach to Interpretable Evaluation
arXiv:2603.23517v1 Announce Type: new 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.
arXiv:2603.23517v1 Announce Type: new 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.
Executive Summary
This article introduces a novel approach to interpretable evaluation in machine learning, focusing on mechanism-aware assessment that combines symbolic rules with mechanistic interpretability. The authors argue that accuracy-based evaluation is insufficient for distinguishing genuine generalization from shallow learning strategies like memorization. The proposed symbolic-mechanistic approach is demonstrated on a natural language to SQL (NL-to-SQL) task, highlighting its ability to reveal model failures invisible to traditional accuracy metrics. The authors' findings have significant implications for model evaluation and development in AI, particularly in small-data regimes.
Key Points
- ▸ The limitations of accuracy-based evaluation in distinguishing genuine generalization from shallow learning strategies.
- ▸ The introduction of a symbolic-mechanistic approach to interpretable evaluation, combining symbolic rules with mechanistic interpretability.
- ▸ The demonstration of this approach on the NL-to-SQL task, revealing model failures invisible to traditional accuracy metrics.
Merits
Strength
The proposed symbolic-mechanistic approach offers a novel and much-needed solution to the limitations of accuracy-based evaluation in AI model development.
Demerits
Limitation
The approach may require significant computational resources and expertise in symbolic manipulation, potentially limiting its practical applicability.
Expert Commentary
The article makes a significant contribution to the field of AI model evaluation and development, highlighting the limitations of accuracy-based evaluation and proposing a novel solution. The demonstration of the symbolic-mechanistic approach on the NL-to-SQL task is particularly compelling, showcasing its ability to reveal model failures invisible to traditional accuracy metrics. However, the approach may require significant computational resources and expertise in symbolic manipulation, potentially limiting its practical applicability. Nevertheless, the article's findings have significant implications for AI model development and deployment, and its approach may revolutionize the way we evaluate and develop AI models.
Recommendations
- ✓ Further research is needed to develop and refine the symbolic-mechanistic approach, particularly in terms of scalability and computational efficiency.
- ✓ The approach should be applied to a broader range of AI tasks and applications to fully evaluate its potential and limitations.
Sources
Original: arXiv - cs.LG
