Measuring the breadth of a word's meaning, or its spread across contexts, has become feasible with contextualized token embeddings. A word type can be represented as a cloud of token vectors, with dispersion-based statistics serving as proxies for contextual diversity (Nagata and Tanaka-Ishii, ACL2025). These measurements are useful for deciding appropriate sense distinctions when constructing thesauri and domain-specific dictionaries. However, when comparing the breadth of two word types, naive hypothesis testing on dispersion can be misleading: differences in semantic direction can masquerade as dispersion differences, inflating Type-I error and yielding "statistically significant" outcomes even when there is no true breadth difference. This is problematic because significance testing should distinguish genuine effects from incidental fluctuations in small-difference regimes. We propose a Householder-aligned permutation test to isolate dispersion differences from directional differences. Our method applies a single Householder reflection to align the mean directions of the two word types and then performs a permutation test on the aligned token clouds, yielding calibrated, non-parametric p-values. For practicality, we introduce a GPU-oriented implementation that batches permutations and linear algebra operations. Empirically, our alignment reduced Type-I error by 32.5% while preserving sensitivity to genuine breadth differences, and achieved a 23x speedup over the CPU baseline.
Why are language models less surprised than humans? Testing the Parse Multiplicity Mismatch Hypothesis
Department of Linguistics, New York University（纽约大学语言学系）；Center for Data Science, New York University（纽约大学数据科学中心）；Department of Linguistics, University of Massachusetts Amherst（马萨诸塞大学阿默斯特分校语言学系）
From Words to Collisions: LLM-Guided Evaluation and Adversarial Generation of Safety-Critical Driving Scenarios
Professorship of Autonomous Vehicle Systems, TUM School of Engineering and Design, Technical University of Munich（自主车辆系统教授职位，技术大学慕尼黑工程与设计学院）；Munich Institute of Robotics and Machine Intelligence (MIRMI)（慕尼黑机器人与机器智能研究所）；TUM School of Computation, Information and Technology, Department of Computer Engineering, Technical University of Munich（技术大学慕尼黑计算、信息与技术学院，计算机工程系）
