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
arXiv:2607. 08731v1 Announce Type: cross Abstract: A national language model offers a linguistic community its own instrument for measuring what its citizens say and value.
By Manuel Pita
arXiv:2606. 23462v2 Announce Type: replace-cross Abstract: Scientists do not, by profession, wage war.
By Sovesh Mohapatra, David Lydon-Staley, Dani S. Bassett
arXiv:2607. 08731v2 Announce Type: replace-cross Abstract: National language models are becoming publicly funded epistemic infrastructure.
By Manuel Pita
arXiv:2609.25298v1 Announce Type: new
Abstract: Cultural evaluation coverage and robustness in language models are difficult to diagnose because pretraining corpora and cultural benchmarks are rarely...
By Yusser Al Ghussin, Eva Gavaller, Cristina Espa\~na-Bonet, Josef van Genabith, Simon Ostermann
A national language model offers a linguistic community its own instrument for measuring what its citizens say and value. Portugal's AMALIA, a publicly funded 9B-parameter model for European Portuguese, appears competitive on agreement alone: asked to code the moral foundation of authority, it agrees with trained human coders to within six F1 points of open models eight to thirteen times its size.
We present a computational stylometric analysis of the Tipitaka across all three Pitakas in English translation, extending earlier work on the Sutta Pitaka alone. The corpus spans 134,831 segments from Bhikkhu Sujato's Sutta Pitaka (114,591 segments, CC0), Bhikkhu Brahmali's Vinaya Pitaka (7,923 segments, CC0 2026), I.
arXiv:2608. 11816v1 Announce Type: cross Abstract: State-aligned distortion has been documented in China-origin text-based large language models (LLMs), but whether, and in what form, it arises in multimodal systems has not been systematically examined.
By Guang Yang, Fengchen Liu, Alex Wang, Homa Hosseinmardi, Amir Ghasemian
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
arXiv:2607. 13077v1 Announce Type: cross Abstract: Large language models (LLMs) often produce homogeneous outputs, raising concerns that AI coding assistants may lead to convergence in the software artifacts that developers create.
By Gordon Burtch
arXiv:2606. 28335v1 Announce Type: cross Abstract: We argue, with systematic empirical evidence, that a large language model's political ideology is not a fixed point, but a conditional distribution $\mathbb{P}($position$\mid$context$)$ over a real political space.
By Adib Sakhawat, Syed Rifat Raiyan, Tahsin Islam, Takia Farhin, Hasan Mahmud, Md Kamrul Hasan
The paper argues that computational text‑based ideal point estimation (CT‑IPE) methods should be viewed as configurable measurement pipelines rather than fixed estimators. It presents a large‑scale comparative experiment involving 17 CT‑IPE algorithms, 5,537 runs, and about 4.25 million left‑right position estimates, and describes shared infrastructure that enables joint execution of these heterogeneous methods. Sensitivity analyses reveal that most algorithms exhibit low hyperparameter sensitivity (ICC < .10), with any remaining sensitivity concentrated in a few key researcher choices such as the language or embedding model, seed keyword lists, and number of topics.
By Patrick Parschan