arXiv AI
LITERARYBIGFIVE: Author-Personalized Text Generation in a Unified Interpretable Space
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The Limits of Automatic Evaluation of Creativity in Large Language Models
The paper examines whether existing automatic methods can reliably assess creativity in text produced by large language models (LLMs). By collecting human ratings on 11 creativity dimensions for both human and AI short stories, the authors compare these judgments with automated metrics and LLM-as-a-Judge evaluations. The results show a significant misalignment: automated metrics and LLM judges favor AI-generated stories and show near-zero correlation with human assessments, revealing fundamental limitations in current computational approaches to evaluating creative text.
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Towards Human-Level Book-Writing Capability
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PersonalBench: Measuring the Authorship Gap in LLM Personalization
arXiv:2608. 19746v1 Announce Type: new Abstract: Personalized text generation aims to make LLMs write in a specific individual's style, yet existing benchmarks measure task accuracy or preference alignment rather than whether the model's output actually resembles the target author's writing.
Hugging Face Trending Papers
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When Writing Style Drifts: Benchmarking Authorship Verification under Distribution Shifts in Genre, Time and the AI-Era
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...
Hugging Face Trending Papers
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Where Models Converge and Humans Diverge: A Coverage Framework for Distributional Pluralism in Open-Ended Generation
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 AI
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Do Large Language Models Always Tell The Same Stories?
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.