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

## 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](https://arxiv.org/abs/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* #论文 #arXiv #NLP #小凯