The study examines how emotions are represented across layers of large language models (LLMs) by probing eight 1B–9B open‑weight models on three datasets (Twitter, Reddit, autobiographical narratives). It finds that the optimal probing layer varies systematically with the dataset, moving from near‑input layers to deeper layers, and that targeted forward‑pass interventions on these layers degrade performance more than random interventions. Additionally, the selected layers transfer across datasets and emotion categories, and early‑exit representations from these layers outperform full‑depth exits by an average of 6.9 percentage points.
By Tian Fang, Ga\"el Guibon, Davide Buscaldi
arXiv:2604. 07801v2 Announce Type: replace-cross Abstract: Large language models are trained and evaluated on quantitative reasoning tasks written in clean, emotionally neutral language.
By Atahan Dokme, Benjamin Reichman, Larry Heck
arXiv:2606.26040v2 Announce Type: replace
Abstract: AI translation of literary works is increasingly common. While the content may be rendered adequately, we do not know enough about how readers expe...
By Yves Ferstler, Adam Podoxin, Ty Brassington, Ga\"elle Laperri\`ere, Roman Grundkiewicz, Marie-Jean Meurs, Maite Taboada, Marzena Karpinska
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...
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:2606. 27717v1 Announce Type: cross Abstract: Prosodic emphasis varies across languages, emotions, and speaking styles, yet existing emphasis detection models are largely trained and evaluated on monolingual neutral read speech.
By Megan Wei, Deepali Aneja, Jiaqi Su, Yunyun Wang, Haonan Chen, Zeyu Jin
The paper explores how to improve literary machine translation by using datasets that contain multiple valid translations of the same source text. It introduces a filtering framework that selects source texts whose references show meaningful variation while staying faithful, based on semantic similarity. Experiments show that fine‑tuning on medium to high similarity data outperforms low similarity data, and that using only this filtered subset can match or exceed performance achieved with the full unfiltered set. Additionally, the study compares synthetic translations generated by large language models with human expert translations, finding that fine‑tuning on human expert data yields better results in both automatic metrics and human evaluations, underscoring the continued importance of expert translations for literary MT.
By Si Wu, John Wieting, David A. Smith
arXiv:2601. 15828v4 Announce Type: replace-cross Abstract: This study investigates whether professional translators without prior specialized training can reliably identify short stories generated in Italian by artificial intelligence (AI).
By Michael Farrell
Sofroniew et al. (2026) showed that emotion concepts in Claude Sonnet 4.5 are encoded as vectors whose geometry mirrors human affect psychology. This study replicates that finding using the base pretrained model google/gemma-2-27b, generating 205,200 Claude Sonnet 4.5 stories, extracting 171 emotion vectors, and recovering a similar affective circumplex with principal components explaining comparable variance. The analysis further identifies a sharp geometric seam at layers 22‑26, demonstrates that much of the geometry already exists in static token embeddings, and shows that the geometry predicts token‑level co‑activation with high correlation.
By Adam Hollowell
CHRONOBERG is a temporally structured corpus of English book texts covering 250 years, curated from Project Gutenberg and enriched with temporal annotations. It enables quantification of lexical semantic change via time‑sensitive Valence‑Arousal‑Dominance analysis and the creation of historically calibrated affective lexicons. Experiments show that language models trained sequentially on CHRONOBERG struggle to encode diachronic shifts, highlighting the need for temporally aware training and evaluation pipelines.
By Niharika Hegde, Subarnaduti Paul, Lars Joel-Frey, Manuel Brack, Kristian Kersting, Martin Mundt, Patrick Schramowski
arXiv:2607. 20241v1 Announce Type: cross Abstract: Culturally loaded translation poses unique challenges for machine translation (MT), as meanings are deeply embedded in socio-cultural contexts beyond surface linguistic forms.
By Yiming Wang, Jiayuan Di
arXiv:2606. 17350v1 Announce Type: cross Abstract: Recent advances in large language models (LLMs) have enabled the generation of high-quality prose, yet the question of whether these models are capable of generating diverse outputs remains contested.
By Thennal DK, Hans Ole Hatzel