arXiv:2609.15511v1 Announce Type: cross Abstract: This paper investigates the generation and human evaluation of Japanese haiku by contemporary Large Language Models (LLMs), focusing on authorship pe...
arXiv:2606. 24093v1 Announce Type: cross Abstract: We ask whether the geographic origin of Tang-dynasty poets leaves a detectable linguistic trace in their work.
Recently, large language models (LLMs) have achieved promising progress in the fields of classical Chinese translation and the generation of classical poetry. However, domain-specific research on precise translation and affective-semantic understanding of classical poetry remains limited.
arXiv:2606. 12392v1 Announce Type: cross Abstract: Recently, large language models (LLMs) have achieved promising progress in the fields of classical Chinese translation and the generation of classical poetry.
arXiv:2609.23951v1 Announce Type: new Abstract: Expressive speech synthesis has advanced through prosody modeling, yet generating structured poetic speech, such as haiku, remains challenging. Prior w...
The study demonstrates that text produced by large language models (LLMs) leaves a distinct stylometric footprint—primarily increased entropy and lexical diversity—across multiple models and domains. In contrast, AI editing of human text does not replicate this footprint; edited texts show only modest lexical diversity gains and reduced entropy, with lexical density emerging as the key distinguishing feature. Consequently, stylometric analysis can differentiate AI-generated from AI-edited content, but is less effective at distinguishing either from purely human writing.
arXiv:2609. 28245v1 Announce Type: cross Abstract: Large language models (LLMs) have shown strong performance in creative text generation, yet their ability to produce culturally grounded and stylistically constrained literary forms remains underexplored.
The paper introduces a training‑free method for uncovering prompt‑conditional stylistic axes in large language models (LLMs). By repeatedly sampling completions of a single prompt at high temperature and applying Principal Component Analysis (PCA) to the pooled hidden activations, the authors automatically label the resulting axes using the extreme (pole) generations. Validation against 245 human‑elicited stylistic annotations shows that, for the Qwen‑3.5‑4B‑Instruct model, the top two axes align with human dimensions with 72.8% precision and 43.6% macro‑recall, and 75.6% of validity ratings confirm the axes’ polar generations, while other models exhibit varying degrees of discoverability.
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...
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...
The paper investigates training‑free detection of machine‑generated text using spectral analysis. It shows that spectral energy correlates with variance in token probability trajectories and that human writing produces characteristic fluctuations, termed "generative vitality." The authors find that spectral signals are strongest for long, continuous, constrained generations, while shorter or mixed texts require additional confidence‑based metrics.
The paper evaluates five large language models as zero‑shot annotators of four social constructs—self‑esteem, self‑control, seeking belonging, and seeking recognition—in English song lyrics. It examines repeated‑measurement reliability, cross‑model convergence, and the transferability of consensus labels to supervised classification. Results show varying reliability across constructs, with self‑esteem being most stable and seeking recognition least stable, and indicate that consensus labels contain learnable signal for downstream tasks.