The paper shows that the Flesch Reading Ease and Flesch‑Kincaid Grade Level scores, which are computed from the same two document statistics, converge almost surely to deterministic functions of a document’s topic distribution when modeled with a topic model that includes explicit sentence boundaries. In the long‑text limit, all variation in these scores is driven solely by topical composition, not by any residual readability signal. Experiments on the Brown and BNC corpora demonstrate that a topic vector inferred from one half of a document can predict the other half’s FKGL with substantial correlation (r = 0.779 and 0.884), though adding this prediction to genre and syllable‑count features yields only marginal gains in explained variance.
By Yo Ehara
arXiv:2608. 12630v1 Announce Type: cross Abstract: While large language models can generate entire novels, there is little information about the level of formal variation in their output over many generations.
By Mehdy Sedaghat Payam, Justin Quinn
Authorship verification (AV) assumes that an author's writing style remains sufficiently stable to distinguish it from that of other writers. In practice, however, this assumption is challenged by dis...
arXiv:2608.23124v1 Announce Type: cross
Abstract: Personalized text generation for authors and literary writing is essential for applications such as adaptive writing assistants, creative support too...
By Jinghui Zhang, Lang Gao, Ao Li, Mingzhe Li, Ruihong Zeng, Zirui Song, Kentaro Inui, Xiuying Chen
arXiv:2310.00436v2 Announce Type: replace
Abstract: Authorship identification uses patterns in writing to infer who wrote a text, but those patterns also reflect topic, genre, and register. This surv...
By Haining Wang
arXiv:2508. 01656v2 Announce Type: replace-cross Abstract: As Large Language Models (LLMs) have reached human-like fluency and coherence, distinguishing machine-generated text (MGT) from human-written content becomes increasingly difficult.
By Lucio La Cava, Dominik Macko, R\'obert M\'oro, Ivan Srba, Andrea Tagarelli
Personalized text generation for authors and literary writing is essential for applications such as adaptive writing assistants, creative support tools, and computational literary analysis. However, e...
An end-to-end classical NLP experiment on Kaggle’s Spooky Author Identification task: from Vowpal Wabbit and TF-IDF/NB-SVM baselines to a tuned stacked ensemble, with a compact representation survey of Bag-of-Words, BM25, Word2Vec, and FastText for context. The post How Far Can Classical NLP Go?
By Nahid Ahmadvand
arXiv:2604. 26269v2 Announce Type: replace-cross Abstract: In the era of large language models, creative writing quality lacks a computable theoretical anchor.
By Bo Zou, Chao Xu
arXiv:2607. 09316v1 Announce Type: cross Abstract: Thematic indexing -- the practice of assigning structured conceptual labels to sections of text -- is essential to scholarly access in large-scale literary and historical editions, yet it remains a largely manual, labour-intensive process.
By Miguel Arana-Catania, Gillian Pink, Glenn Roe
When a large language model (LLM) writes Harry Potter fanfiction, it reliably produces fundamental elements of the Hogwarts universe, such as recognizable places and characters. Human-written Harry Potter fanfictions, however, typically include these fundamentals and much more, incorporating stylistically irregular content and relationship-diverse plotlines.
arXiv:2607. 27266v1 Announce Type: cross Abstract: We propose a statistical methodology that quantifies the similarity of typefaces between printed historical books.
By Diego Belzarena (UDELAR, CB), Seginus Mowlavi (CB), Paula Casariego Casti\~neira (ROMA TRE), Alejandra Ulla Lorenzo (USC), Gregory Randall (UDELAR), Jean-Michel Morel (LU - Hong Kong)