Learning by Surprise: Adaptive Mitigation of Model Collapse in Large Language Models
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Computer Science > Computation and Language
arXiv:2410.12341 (cs)
[Submitted on 16 Oct 2024 (v1), last revised 30 Jun 2026 (this version, v4)]
Title:Learning by Surprise: Adaptive Mitigation of Model Collapse in Large Language Models
Authors:Daniele Gambetta, Gizem Gezici, Fosca Giannotti, Dino Pedreschi, Alistair Knott, Luca Pappalardo
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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. This feedback loop has been shown to induce model collapse, typically characterized by a loss of diversity in generated content. However, existing work offers a limited understanding of this phenomenon and relies on mitigation strategies that assume access to human-authored data. In this paper, we conduct extensive simulations across multiple datasets and LLMs to address key gaps in the study of model collapse. First, we introduce model-intrinsic measures based on next-token probability distributions, showing that model collapse corresponds to an increasing concentration of probability mass on a small set of tokens. Second, we demonstrate that model collapse is also associated with a loss of common sense, as measured by a decline in commonsense inference accuracy. Third, we identify perplexity (a measure of model "surprise") as a key driver of collapse: fine-tuning on the least "surprising" documents leads to more severe degeneration. Building on this insight, we propose a perplexity-based filtering strategy that prioritizes high-surprise documents during fine-tuning. Unlike existing approaches, our method does not require distinguishing between human-authored and AI-generated content. Across datasets and LLM families, this strategy consistently mitigates model collapse, achieving performance comparable to, and in some cases better than, human-data baselines, while substantially reducing the concentration of next-token probabilities. Overall, our results provide a unified, model-centric understanding of model collapse and suggest practical, scalable strategies for training generative AI systems in increasingly synthetic environments.
| Subjects: | Computation and Language (cs.CL); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2410.12341 [cs.CL] |
| (or arXiv:2410.12341v4 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2410.12341
arXiv-issued DOI via DataCite
|
Submission history
From: Daniele Gambetta [view email][v1] Wed, 16 Oct 2024 08:02:48 UTC (1,943 KB)
[v2] Sun, 2 Feb 2025 22:40:09 UTC (348 KB)
[v3] Tue, 2 Sep 2025 13:11:32 UTC (336 KB)
[v4] Tue, 30 Jun 2026 13:18:28 UTC (286 KB)
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View a PDF of the paper titled Learning by Surprise: Adaptive Mitigation of Model Collapse in Large Language Models, by Daniele Gambetta and 5 other authors
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