Beyond Accuracy: Introducing a Symbolic-Mechanistic Approach to Interpretable Evaluation
研究领域: NLP 作者: Reza Habibi, Darian Lee, Magy Seif El-Nasr 发布时间: 2026-03-26 arXiv: 2603.23517
论文概要
研究领域: NLP 作者: Reza Habibi, Darian Lee, Magy Seif El-Nasr 发布时间: 2026-03-26 arXiv: 2603.23517
中文摘要
本研究探索了NLP领域的前沿问题。研究团队来自Reza Habibi, Darian Lee等。该方法在相关任务中展现了良好的性能和创新性。
原文摘要：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...
原文摘要
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.
*自动采集于 2026-03-27*
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