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
arXiv:2607. 05679v1 Announce Type: cross Abstract: Language models (LMs) exhibit problematic biases, such as stereotypes.
By Damian Hodel, Jevin West, Aylin Caliskan
arXiv:2607. 04223v1 Announce Type: cross Abstract: Retrieval-augmented generation (RAG) reduces but does not eliminate hallucination, and existing detectors return a single answer-level score that does not indicate which sentence is unsupported, or why.
By Mohamed Aly Bouke
arXiv:2606. 08417v1 Announce Type: cross Abstract: Diffusion and continuous flow-based language models have emerged as the leading non-autoregressive alternatives to language modeling.
By Antonio Franca, Alexander Tong
arXiv:2509. 25359v2 Announce Type: replace-cross Abstract: We present a systematic stress-test of geometric metrics for LLM evaluation.
By Viacheslav Yusupov, Anna Antipina, Ameliia Alaeva, Danil Maksimov, Anna Vasileva, Tatyana Zaitseva, Alina Ermilova, Evgeny Burnaev, Egor Shvetsov
arXiv:2603.04419v3 Announce Type: replace-cross
Abstract: Vision-language models produce different object and use descriptions under different persona prompts, but low overlap alone does not identify...
By Murad Farzulla
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.
The paper introduces Variance‑Calibrated Modulation (VCM), a training‑free pre‑decoding technique that reshapes language model probability distributions before truncation. VCM uses two dynamic mechanisms: a Contextual Searchlight via PMI to suppress stopwords and highlight context‑relevant tokens, and an Adaptive Self‑Debiasing that applies scale‑invariant penalization based on real‑time logit standard deviation. Experiments on open‑ended generation, factual QA, and mathematical reasoning show that VCM consistently reduces the likelihood trap, improving diversity, coherence, and reasoning accuracy with minimal computational cost.
By Yuanhao Ding, Meimingwei Li, Esteban Garces Arias, Matthias A{\ss}enmacher, Christian Heumann, Chongsheng Zhang
arXiv:2608.30754v1 Announce Type: new
Abstract: Evaluating creativity in large language model (LLM) outputs remains challenging because creativity is multidimensional and human-centered. We examine h...
By Mohammad Reza Modarres, Armin Tourajmehr, Yadollah Yaghoobzadeh, Mohammad Taher Pilehvar
The paper examines whether adding syntactic and rhetorical structure to text can improve the prediction of incoherence in large language model outputs. Experiments show that plain text actually yields higher accuracy, as the added structural information conflicts with the models’ architectures. The authors also demonstrate that coherence assessment can help detect misleading content by applying zero‑shot experiments to a Brazilian disinformation dataset.
By Victor Mazzotti, Luiz Pereira, Marina Bitencourt dos Santos, Helena Maia, Carlos Caetano, N\'adia Felix, Sandra Avila
arXiv:2609.14743v1 Announce Type: new
Abstract: Pure speech language models often lag behind text and speech-text language models in generating coherent content, but this gap is difficult to quantify...
By Ju-Chieh Chou, Jiawei Zhou, Karen Livescu
The article presents a technical manual for an open toolkit designed to measure how transformer language models individuate word meanings across different contexts. It introduces the concept of a "bridge form"—a single word that appears unchanged in multiple domains but with distinct senses—and outlines a full pipeline from specifying these forms to extracting layer-wise representations, computing silhouette-based separation metrics, and visualizing results. The manual details each design choice and its intended methodological safeguards, emphasizing that it serves as a methodological reference rather than reporting empirical findings.
By Jos\'e Luciano Ver\c{c}osa Marques, Frederico Jorge Heitmann, Daniel Omar Perez, Marcelo Vinicius de Paula, T\'arcio Andr\'e dos Santos Barros