arXiv:2608. 19472v1 Announce Type: new Abstract: Lexical semantic change (LSC) is commonly modelled through vector-space representations, but these approaches often provide limited insight into which aspects of usage are changing.
By Bach Phan-Tat, Kris Heylen, Dirk Geeraerts, Stefano De Pascale, Dirk Speelman
arXiv:2609.08609v2 Announce Type: replace
Abstract: Tracking semantic change in low-resource languages across extensive historical timelines presents significant challenges due to data scarcity and t...
By Nevidu Jayatilleke, Nisansa de Silva
The BD-LSC dataset introduces a bi‑directional lexical semantic change benchmark that tracks sense gain, loss, and stability across three time periods, while the ST‑WSD dataset offers fine‑grained, instance‑level sense annotations for words that blend slang and standard usage. These resources enable systematic evaluation of diverse models—including unsupervised clustering, supervised learning, transformer‑based approaches, and large language models—on tasks such as exact sense matching and multi‑label accuracy. The evaluation shows that few‑shot GPT‑4o performs best overall, yet all systems struggle with rare slang senses, highlighting a key open challenge in the field.
By Afnan Aloraini, Riza Batista-Navarro
The study investigates how disrupting conceptual versus referential information in short narratives affects human reading and large language model (LLM) processing. In humans, conceptual disruptions cause a strong, localized processing cost that peaks early and declines quickly, while referential disruptions produce weaker, gradually decreasing effects that are more influenced by sentence boundaries. In LLMs, both disruptions appear immediately at the manipulated word; surprisal patterns mirror human reading, whereas output-layer representations show that referential disruption initially causes a larger displacement before both types decay following a power-law.
By Rui He, Nihal Altay, Wolfram Hinzen
arXiv:2606. 11371v1 Announce Type: cross Abstract: Spoken language, whether produced by humans or large language models (LLM), unfolds over time with varying semantic content.
By Han-Jen Chang, Yasir \c{C}atal, Angelika Wolman, Agust\'in Ib\'a\~nez, David Smith, I-Wen Su, Kai-Yuan Cheng, Georg Northoff
arXiv:2608. 03507v1 Announce Type: cross Abstract: Historical language change affects morphology, syntax, semantics, and pragmatics, yet computational studies typically examine these levels with incompatible representations and therefore cannot determine whether they evolve together across languages.
By Gagan Bhatia, Julian Schlenker, Simone Paolo Ponzetto, Steffen Eger