arXiv AI

A Cognitively Motivated Multidimensional Framework for Evaluating Metaphor Explanations

arXiv:2608. 15828v1 Announce Type: cross Abstract: Current evaluation of metaphor explanations relies mainly on holistic quality ratings, revealing little about how explanation quality is structured or where human judgments agree and diverge.

arXiv AI
Jun 26

Metaphors are a Source of Cross-Domain Misalignment of Large Reasoning Models

arXiv:2601. 03388v3 Announce Type: replace-cross Abstract: Earlier research has shown that metaphors influence human decision-making, raising the question of whether metaphors also influence large language models (LLMs)' reasoning pathways, given that their training data contain a large number of metaphors.

By Zhibo Hu, Chen Wang, Yanfeng Shu, Hye-young Paik, Liming Zhu
arXiv Machine Learning
Aug 5

VIVID: A Culturally Grounded Benchmark Exposing the Figurative Language Gap in Vietnamese NLP

arXiv:2608. 03095v1 Announce Type: cross Abstract: We present VIVID (Vietnamese Idioms for Validation and Interpretation Depth), the first systematic benchmark for evaluating culturally grounded figurative language understanding in Vietnamese.

By Tu Tran Do, Nhat Ngoc Nguyen, Khanh-Tung Tran, Hoang D. Nguyen, Tu Minh Phuong, Long Hoang Dang
Hugging Face Trending Papers
Jul 20

Computational models of pragmatic reasoning with flexible generation of meaning and expression alternatives

Pragmatic language use requires reasoning about alternatives: the alternative expressions a speaker might have chosen, or the alternative interpretations a listener might entertain. Formal and computational models of pragmatics must therefore specify the sets of alternatives that interlocutors reason over, which is often done through manual specification.

arXiv AI
Jul 15

Toward Metaphor-Fluid Conversation Design for Voice User Interfaces

arXiv:2502. 11554v3 Announce Type: replace-cross Abstract: Metaphors play a critical role in shaping user experiences with Voice User Interfaces (VUIs), yet existing designs often rely on static, human-centric metaphors that fail to adapt to diverse contexts and user needs.

By Smit Desai, Jessie Chin, Dakuo Wang, Benjamin Cowan, Michael Twidale