# Feedback loop and baseline contamination > Source-anchored research for Feedback loop and baseline contamination. Run status: COMPLETED. Parent: [Human adoption of LLM vocabulary and its effect on tic baselines](https://llms-explorer.com/tree/ai-isms-human-convergence/) · 2 facets · 4 facts · page: https://llms-explorer.com/tree/feedback-loop-and-baseline-contamination/ ## Problems, failure modes and limitations - Model collapse under a replace-data regime (each generation's synthetic data replaces the prior data) degrades models, but if synthetic data accumulate alongside the original real data, test error has a finite upper bound independent of iteration count; this held across language models, diffusion models and VAEs. (medium confidence) — [source](https://arxiv.org/abs/2404.01413) ## Facts and statements - A Max Planck study of 737,083 hours of podcast speech (824,634 episodes) found abrupt post-ChatGPT increases in words such as delve, showcase, boast, intricacies and meticulous; synthetic-control analysis supports a causal link, and the authors frame it as machines trained on human data feeding their traits back into human language. (low confidence) (verified as of 2026-07-16) — [source](https://arxiv.org/abs/2409.01754) - A preregistered experiment (N=496) found brief chatbot interaction led participants to adopt the chatbot's words, persisting through a distractor task and in forced lexical choice. (low confidence) — [source](https://arxiv.org/abs/2409.01754) - The term 'model collapse' is used inconsistently in the literature and is conflated with generic performance loss or any synthetic-data training; critics argue claims of inevitable collapse need scoping to the specific definition and data regime. (low confidence) — [source](https://arxiv.org/pdf/2503.03150)