The paper investigates whether large language models (LLMs) capture the full diversity of outputs present in their training data. Using an information‑theoretic approach, the authors compare the conditional entropy of model‑generated outputs with that of the training data, finding that LLMs consistently produce outputs with lower conditional entropy across various models, scales, and decoding strategies. They also extend the analysis to image and text‑conditioned generators, propose a post‑hoc correction method based on matrix‑entropy projection to increase conditional diversity, and provide theoretical guarantees and an efficient algorithm for this correction.
By Youqi Wu, Farzan Farnia
The paper investigates how long, richly detailed prompts cause modern text-to-image models to lose diversity, even when many visual aspects are unspecified. It introduces PromptMoG, a training‑free method that samples prompt embeddings from a Mixture‑of‑Gaussians distribution to restore diversity while preserving semantic fidelity. The authors also present LPD‑Bench, a benchmark for evaluating fidelity and diversity under long, semantically dense prompts, and demonstrate PromptMoG’s effectiveness on four large diffusion models.
By Bo-Kai Ruan, Teng-Fang Hsiao, Ling Lo, Yi-Lun Wu, Hong-Han Shuai
The paper argues that measuring diversity in AI-generated content using a single scalar score is inherently ambiguous and often misleading. It reviews existing diversity metrics, demonstrates their limitations through axiomatic and empirical analyses, and introduces diversity profiles—curve-valued, condition-aware summaries that evaluate diversity across a range of thresholds, scales, exponents, or orders. These profiles reveal whether comparisons are robust across resolutions or depend on arbitrary parameter choices, offering a more transparent framework for generative AI evaluation.
By Xiuyuan Hu, Xuege Hou, Guoqing Liu, Yang Zhao, Jieran Li, Dongbiao Sun, Jos\'e Miguel Hern\'andez-Lobato, Hao Zhang, Xue Liu
arXiv:2608. 09385v1 Announce Type: cross Abstract: Generative AI models are primarily designed to imitate the data distribution, an objective that neither corrects diversity lost by a learned generator nor defines how generation should extend beyond the diversity of the data itself.
By Hossein Goli, Farzan Farnia, Amin Gohari
arXiv:2307.00852v3 Announce Type: replace
Abstract: The natural language generation domain has witnessed great success thanks to Transformer models. Although they have achieved state-of-the-art gener...
By Yueen Ma, Dafeng Chi, Jingjing Li, Kai Song, Yuzheng Zhuang, Irwin King
arXiv:2608.29335v1 Announce Type: new
Abstract: Latent generative models typically follow a two-stage pipeline, training a variational autoencoder for reconstruction and then a generative model on th...
By Guangting Zheng, Yiyuan Zhang, Tao Yang, Yunpeng Chen, Rui Zhu, Jiajun Deng, Yanyong Zhang
Generative AI models are primarily designed to imitate the data distribution, an objective that neither corrects diversity lost by a learned generator nor defines how generation should extend beyond the diversity of the data itself. We introduce Imaginative Generative AI (IGA), a framework that makes diversity part of the target-distribution design problem: among distributions close to a reference, IGA selects one whose spectral diversity reaches a prescribed level.
arXiv:2606. 01811v1 Announce Type: cross Abstract: Measuring the diversity of creative outputs is central to evaluating post-training mode collapse, comparing decoding strategies, and quantifying creative behavior in both AI and human writing.
By Matthew Khoriaty, David Williams-King, Shi Feng
The paper introduces a new measure of generative‑process diversity for language models, using Normalised Compression Distance on raw outputs after controlling for permutation effects. Across 38 models, this metric uncovers population structure that semantic similarity misses and predicts lower correlated failures across ten benchmark families, independent of semantic similarity or model capability. The authors argue that higher generative‑process diversity reduces correlated failures in multi‑model systems, offering a practical tool for safety‑relevant applications.
By Ross Tieman, Evan Markou
The paper introduces a method called Debias Anything that jointly addresses fairness and diversity in diffusion models without requiring sensitive-attribute annotations. By connecting a frozen diffusion model to a pretrained vision-language embedding space via an adapter, the approach uses pairs of text prompts to guide batch composition toward desired attribute proportions and employs a disagreement score to promote diversity. The method is applicable to both unconditional and text-conditional diffusion models and demonstrates improved quality and diversity while maintaining comparable fairness levels in experiments.
By Th\'eau d'Audiffret, Mariia Vladimirova, Jean-Yves Franceschi
arXiv:2511.19811v2 Announce Type: replace-cross
Abstract: Image diversity remains a fundamental challenge for text-to-image diffusion models. Low-diversity generation often leads to repetitive output...
By Debin Meng, Chen Jin, Zheng Gao, Yanran Li, Ioannis Patras, Georgios Tzimiropoulos
PAPT++ is a risk‑aware adversarial generation‑training framework designed to improve single domain generalization. It learns diverse semantic reference images per class and uses them as denoising targets in classifier‑guided diffusion synthesis, thereby generating challenging yet semantically consistent samples. These samples are iteratively combined with source data to update the classifier, progressively exposing it to difficult variations and enhancing generalization performance on standard benchmarks.
By Zhipeng Xu, De Cheng, Xinyang Jiang, Lingfeng He, Huaijie Wang, Dongsheng Li, Nannan Wang, Xinbo Gao