arXiv Machine Learning June 2, 2026 By Devon Jarvis, Richard Klein, Benjamin Rosman, Steven James, Stefano Sarao Mannelli

Position: the Stochastic Parrot in the Coal Mine. Model Collapse is a Threat to Low-Resource Communities

Read the original on arXiv Machine Learning →

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.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

llms

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.

By Damian Hodel, Jevin D. West

llms

arXiv AI

Aug 25

Reviewing Model Collapse and Countermeasures

arXiv:2608.21366v1 Announce Type: new Abstract: Driven by massive amounts of web-scale data, generative AI (GenAI) has achieved remarkable progress, enabling various applications in diverse sectors....

By Xihao Xie, Beichen Hu

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.

By Daniele Gambetta, Gizem Gezici, Fosca Giannotti, Dino Pedreschi, Alistair Knott, Luca Pappalardo

llms fine-tuning

arXiv AI

Jun 6

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.

By Zilong Liu, Krzysztof Janowicz, Gengchen Mai, Song Gao, Rui Zhu

llms benchmarks safety

arXiv AI

Jun 29

Can Generative Artificial Intelligence Survive Data Contamination? Theoretical Guarantees under Contaminated Recursive Training

arXiv:2602. 16065v2 Announce Type: replace-cross Abstract: As artificial intelligence (AI)-generated content proliferates, models are increasingly trained on their own outputs, risking progressive degradation or collapse.

By Kevin Wang, Hongqian Niu, Didong Li

safety

arXiv Machine Learning

Sep 18

The Environmental Impacts of Language Model Training Keep Rising Now is the Time to Catch Impacts on the Rebound

The paper analyzes the environmental footprint of machine learning model training, focusing on large language models and their hardware. It finds that energy use and environmental impacts have risen exponentially over the past decade, even when employing carbon‑efficient electricity and more efficient hardware. The study argues that optimization strategies alone cannot curb these impacts due to a rebound effect, and stresses the need to evaluate hardware life‑cycle impacts and integrate environmental metrics into NLP research practices.

By Cl\'ement Morand (STL), Anne-Laure Ligozat (ENSIIE, LISN, STL), Aur\'elie N\'ev\'eol (STL, LISN)

llms nlp benchmarks

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