arXiv:2606. 29540v1 Announce Type: cross Abstract: Large language models (LLMs) can leave subtle stylistic traces in assisted text; one of the most cited is the em-dash (Unicode U+2014).
By Przemys{\l}aw Czuma
arXiv:2608. 09093v1 Announce Type: cross Abstract: How a document's arrangement is written down, its notation, is a training variable that no dataset card records.
By E. M. Freeburg
arXiv:2608. 16390v1 Announce Type: cross Abstract: PDF corpora advertise their size in tokens but compute every rate they publish (coverage, OCR routing, re-fetch recovery, language mix) per document, and none decomposes its token total.
By Luca Foppiano
The study analyzes 207,111 astronomy papers from 2015 to mid‑2026 to quantify how many contain language‑model‑generated vocabulary. Using a hierarchical Bayesian model calibrated on pre‑2020 unassisted papers and 392 papers that disclose model use, the authors estimate that in 2025 roughly 54% (±8% statistical, ±26% systematic) of papers show a language‑model trace, with the estimate remaining above 36% under various assumptions. Despite only 0.81% of 2025 papers explicitly declaring model assistance, the trace is pervasive, and the detectable signal is fading as authors adapt to the characteristic words.
whyItMatters":"The findings reveal that language‑model assistance has become widespread in recent astronomy research, yet most authors do not disclose its use, highlighting a growing gap between actual practice and transparency in scholarly writing."
By Serat M. Saad, Yuan-Sen Ting
The paper introduces TSS (Triple-Stream Stress probe), a diagnostic framework that splits text into lexical, morpho-syntactic, and psycholinguistic style channels to analyze mental health NLP classifiers. Across four English datasets, TSS uncovers a lexical interference effect where adding lexical features harms performance on human-labeled data but not on auto-labeled data, and proposes the Degree of Divergence (DoD) statistic to audit label-source bias. The study demonstrates that style features largely remain effective even after masking content words, emphasizing that shortcut learning is label-source specific rather than clinically relevant.
By Moustafa Yehia Hassan
arXiv:2609.08372v2 Announce Type: replace
Abstract: A dense retriever encodes a question as one vector, but the question arrives one word at a time. We read 2,144 held-out headlines from Thu Vien Pha...
By Tran Minh Quan