arXiv:2606. 04177v1 Announce Type: cross Abstract: Interpretable linguistic features offer a promising approach for explaining why a given text appears machine-generated, particularly for non-expert users.
By Yassir El Attar, Esra D\"onmez, Maximilian Maurer, Agnieszka Falenska
Granuscore is a reference‑free metric that measures the granularity of text by exploiting the structure of a hierarchical embedding space. It successfully reproduces known hierarchical orderings on the Granola‑EQ dataset, distinguishes granularity across different discourse contexts, and explains sentence‑specificity variations beyond sentence length. The authors also apply Granuscore to four question‑answering benchmarks, revealing systematic differences in granularity among questions, gold answers, and model outputs, thereby offering a new lens for assessing QA dataset difficulty.
By Lukas Ellinger, Alexander Fichtl, Miriam Ansch\"utz, Georg Groh
arXiv:2608.30614v1 Announce Type: new
Abstract: Organizing unstructured feedback text into hierarchical taxonomy is a fundamental challenge in NLP, particularly in domains where feedback arrives at m...
By Sandeep Sricharan Mukku, Albert Aristotle Nanda, Rohit Pyati
The paper presents a large‑scale empirical study of 284 interpretable linguistic features to detect AI‑generated English text across 30 language models and 10 text domains. It finds that classifiers using only linguistic features can reliably distinguish machine‑from human‑written text, but most indicators are highly context‑dependent. Only lexical richness measures consistently generalize across model families and domains, offering robust signals for detection.
By Yassir El Attar, Esra D\"onmez, Maximilian Maurer, Agnieszka Falenska
The paper introduces a reference-based framework to analyze coherence and diversity in open-ended text generation. It evaluates these properties by aligning them with human trajectories, comparing them to human continuations, and estimating their likelihood under a human reference distribution. Experiments show that diversity alignment and mean-based comparisons correlate with human quality ratings, while reference likelihood also associates positively, though results vary by configuration.
By Esteban Garc\'es Arias
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.
By Alessandro Tutone, Giorgio Franceschelli, Mirco Musolesi