arXiv AI
Jun 6
Assessing the Geographic Diversity of AI's Platial Representations in Image Generation
arXiv:2606. 05188v1 Announce Type: cross Abstract: (Gen)AI diversity is not merely an ethical issue.
Geographic Bias and Diversity in AI Evaluation
arXiv:2606. 05187v1 Announce Type: cross Abstract: Among the many challenges hindering the responsible development and deployment of AI, arguably none has faced more intense scrutiny than bias in its various forms.
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arXiv AI
Jun 12
Competition and Diversity in Generative AI
arXiv:2412. 08610v3 Announce Type: replace-cross Abstract: Recent evidence, both in the lab and in the wild, suggests that the use of generative artificial intelligence reduces the diversity of content produced.
arXiv Machine Learning
Jul 31
Epistemic diversity across language models mitigates knowledge collapse
arXiv:2512. 15011v3 Announce Type: replace Abstract: Artificial intelligence (AI) increasingly generates the very content used to train future AI systems.
arXiv AI
22h ago
Evaluating the Diversity of AI-Generated Content with Diversity Profiles
arXiv:2608. 17731v1 Announce Type: new Abstract: Diversity is a fundamental criterion for evaluating generative artificial intelligence (AI) systems, yet its measurement remains inherently ambiguous.
arXiv Machine Learning
Jun 2
Position: the Stochastic Parrot in the Coal Mine. Model Collapse is a Threat to Low-Resource Communities
arXiv:2605. 04127v2 Announce Type: replace Abstract: Model collapse, the degradation in performance that arises when generative models are trained on the outputs of prior models, is an increasing concern as artificially generated content proliferates.
arXiv AI
Jul 1
Learning by Surprise: Adaptive Mitigation of Model Collapse in Large Language Models
arXiv:2410. 12341v4 Announce Type: replace-cross Abstract: As AI-generated content increasingly populates the web, generative AI models are at growing risk of being trained on their own outputs, a process known as AI autophagy.
arXiv AI
Jun 2
"I've Seen How This Goes": Characterizing Diversity via Progressive Conditional Surprise
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 AI
Jul 7
Large Language Models Develop Novel Social Biases Through Adaptive Exploration
arXiv:2511. 06148v4 Announce Type: replace-cross Abstract: As large language models (LLMs) are adopted into frameworks that grant them the capacity to make real decisions, it is increasingly important to ensure that they are unbiased.
arXiv AI
Jun 26
Benchmarking Open-Weight Foundation Models for Global AI Technical Governance
arXiv:2606. 26099v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly deployed in artificial intelligence (AI) governance analysis across national and international organisations.
arXiv AI
Jul 17
How Artificial Intelligence LLM Engines Shape the Global Conflict Information Environment
arXiv:2607. 14197v1 Announce Type: new Abstract: Artificial Intelligence (AI) answer engines now field a growing share of the questions that analysts, scholars, and the public ask about issues of peace and conflict.
arXiv AI
Jun 30
Generative AI Literacy Training Improves Intelligence Analysts' Discrimination of Real and AI-Generated Images
arXiv:2606. 28510v1 Announce Type: cross Abstract: Across social and online platforms, people are increasingly exposed to AI-generated images.
arXiv AI
Jul 17
Verbalized Sampling: How to Mitigate Mode Collapse and Unlock LLM Diversity
arXiv:2510. 01171v4 Announce Type: replace-cross Abstract: Post-training alignment often reduces LLM diversity, leading to a phenomenon known as mode collapse.