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.
arXiv:2608. 07460v1 Announce Type: cross Abstract: While post-training improves the capabilities of large language models (LLMs), it generally lowers their output diversity and creativity, negatively impacting tasks that explicitly require creativity (e.
arXiv:2609.14896v1 Announce Type: cross Abstract: A notable byproduct of LLM alignment training is mode collapse: the progressive loss of output diversity that narrows a model's expressivity at infer...
The paper argues that traditional semantic similarity fails to capture the true diversity of language models. It introduces a new metric—generative‑process diversity—measured via Normalised Compression Distance on raw outputs, which reveals hidden population structure among 38 models. This metric predicts lower correlated failures across ten benchmark families, independent of semantic similarity or model capability.
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.
arXiv:2510. 01171v4 Announce Type: replace-cross Abstract: Post-training alignment often reduces LLM diversity, leading to a phenomenon known as mode collapse.
arXiv:2604. 24927v2 Announce Type: replace-cross Abstract: Generating diverse responses is crucial for test-time scaling of large language models (LLMs), yet standard stochastic sampling mostly yields surface-level lexical variation, limiting semantic exploration.
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.
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.
arXiv:2512. 15011v3 Announce Type: replace Abstract: Artificial intelligence (AI) increasingly generates the very content used to train future AI systems.
arXiv:2609.16454v1 Announce Type: new Abstract: Recent work by Doshi and Hauser (2024), Bisbee et al. (2024), and Xie et al. (2026) raises concerns that outputs from large language models (LLMs) tend...
Standard decoding rules for autoregressive language models promote diversity by rescaling the full next-token distribution or truncating its low-probability tail. These strategies overlook a common re...
arXiv:2606. 11762v1 Announce Type: cross Abstract: Large language models (LLMs) have achieved remarkable progress in language understanding, reasoning, and generation, sparking growing interest in their creative potential.