arXiv:2607. 01034v1 Announce Type: cross Abstract: Large language model (LLM)-based conversational agents (CAs) are now ubiquitous, creating new opportunities for AI-mediated behavior change.
By Hasibur Rahman, Smit Desai
arXiv:2606. 11835v1 Announce Type: cross Abstract: Collecting participants' lived experiences is central to design research.
By Zhiqing Wang, Steven Dow
arXiv:2606. 12350v1 Announce Type: new Abstract: The rapid proliferation of large language models (LLMs) raises critical questions about human creativity and individual expression in an era of AI-assisted creation.
By Maria Edwards, Julian Togelius
arXiv:2510. 04120v2 Announce Type: replace-cross Abstract: Large language models (LLMs) achieve strong performance on metaphor detection and interpretation tasks, yet it remains unclear what such behavioral success reveals about metaphor processing.
By Fengying Ye, Shanshan Wang, Lidia S. Chao, Derek F. Wong
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.
By Ana Naveriani, Jakob Suchan, Stefano Zoia, Mehul Bhatt, Antonio Lieto, Gian Luca Pozzato
arXiv:2607. 28683v1 Announce Type: cross Abstract: Large language models benefit from elements in natural language, such as metaphors and analogies in training data and inference input to achieve generalisability across different domains.
By Zhibo Hu, Chen Wang, Yanfeng Shu, Hye-young Paik, Liming Dong, Liming Zhu