The paper introduces a register-aware framework to evaluate how human-like large language models (LLMs) are, focusing on linguistic feature distributions rather than factual correctness. It uses Maximum Mean Discrepancy (MMD) and 67 Biber lexico‑grammatical features to compare LLM‑generated texts with human reference corpora across different registers. Experiments on seven instruction‑tuned, open‑source models across five English datasets show that all LLMs deviate from human baselines, with closeness to human language varying by register and not by model size.
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.
arXiv:2609.01246v1 Announce Type: new Abstract: Current Large Language Models (LLMs) are primarily optimized for written text, often producing outputs that are grammatically correct and helpful yet p...
arXiv:2601. 22888v4 Announce Type: replace-cross Abstract: More than 80% of the 1.
arXiv:2508.06374v4 Announce Type: replace Abstract: With the surge of large language models (LLMs) and their ability to produce customized output, style-personalized text generation--"write like me"-...
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.
The paper introduces VOLM, a framework that quantifies how much original value a human adds to a document beyond what a language model could generate from a task description alone. Unlike existing tools that focus on stylistic detection, VOLM extracts content at varying granularities, reconstructs it with an LLM, and compares these reconstructions to those derived from the task description. Evaluations across news articles, ICLR peer reviews, and argumentative essays show that VOLM can distinguish human-authored texts from LLM-generated ones while remaining robust to content-preserving transformations.
The paper investigates how machine translation can be tailored to specific audiences and intents, a capability enabled by large language models (LLMs). By systematically evaluating purpose-driven MT across 50 languages, 5 model sizes, and 8 text domains, the authors find that explicit instructions significantly improve translation adaptiveness, especially for informal domains, larger models, and higher-resource languages. They also show that traditional MT metrics often penalize adapted translations and that models can self-generate useful instructions from context, closing a large portion of the adaptiveness gap.
arXiv:2605. 28969v2 Announce Type: replace-cross Abstract: If an AI agent makes decisions on a person's behalf, those decisions must align with its user.
arXiv:2605.27268v2 Announce Type: replace-cross Abstract: Modern Large Language Models (LLMs) are often criticized for producing repetitive and homogeneous text, despite possessing vast latent vocabu...
arXiv:2604. 09497v2 Announce Type: replace-cross Abstract: Accurate evaluation is central to the large language model (LLM) ecosystem, guiding model selection and downstream adoption across diverse use cases.
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.
The paper introduces UPHELD, a large benchmark of human-to-human dialogues written by professional script writers, featuring realistic turn densities and over 36,000 per-turn human annotations. It evaluates existing automatic metrics and LLM-as-a-judge methods, finding them unreliable against expert human judgment. Using UPHELD, the authors develop a Mixture-of-Judges framework that improves correlation with human assessments by about 30%.