The paper investigates why Transformers struggle more with structural than lexical compositional generalisation. It argues that this disparity stems from low structural type diversity rather than an inherent limitation of Transformers. By creating linguistically diverse variants of the COGS and SLOG datasets, the authors show that type diversity correlates equally with generalisation in both lexical and structural cases, challenging previous explanations of the difficulty.
By Anssi Moisio, Mathias Creutz, Mikko Kurimo
The study evaluates eleven autoregressive transformer models on English agreement attraction scenarios using a surprisal-based approach. Results show that while transformers match human reading times for prepositional phrase configurations, they perform poorly on object‑extracted relative clauses, with predictions diverging across models and failing to capture human interference patterns. The authors argue that current transformers cannot adequately model human morphosyntactic processing and call for more rigorous, comprehensive testing to avoid misleading conclusions from limited syntactic setups.
By Titus von der Malsburg, Sebastian Pad\'o
Coordination, a fundamental linguistic structure, remains a subject of intense debate, and its exact nature continues to elude theoretical linguistics. A common view holds that only same-category constituents can be conjoined, which has been challenged by the many grammatical unlike coordinations found in natural language.
arXiv:2605.01609v2 Announce Type: replace-cross
Abstract: We find that transformer concept representations systematically anti-concentrate in the spectral tail of the unembedding covariance, encoding...
By Pratyush Acharya, Nuraj Rimal, Habish Dhakal
The article presents a technical manual for an open toolkit designed to measure how transformer language models individuate word meanings across different contexts. It introduces the concept of a "bridge form"—a single word that appears unchanged in multiple domains but with distinct senses—and outlines a full pipeline from specifying these forms to extracting layer-wise representations, computing silhouette-based separation metrics, and visualizing results. The manual details each design choice and its intended methodological safeguards, emphasizing that it serves as a methodological reference rather than reporting empirical findings.
By Jos\'e Luciano Ver\c{c}osa Marques, Frederico Jorge Heitmann, Daniel Omar Perez, Marcelo Vinicius de Paula, T\'arcio Andr\'e dos Santos Barros
The paper studies how transformer representations evolve across layers by examining the intrinsic dimensionality (ID) of token embeddings and their neighborhood structures. It finds that closed‑class tokens expand and collapse earlier than open‑class tokens, and that these changes are linked to shifts in local geometry. The authors compare encoder and decoder models, showing distinct layer‑wise behaviors, and demonstrate that geometric features alone can predict a token’s part‑of‑speech and reveal how semantic content changes across layers.
By Samuele Vallisa, Federico Ravenda, Claudio Palominos, Rui He, Andrea Raballo, Antonietta Mira, Philipp Homan, Wolfram Hinzen