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

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