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#modelcollapse

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Nayla Salibi

هل نواجه "تلوّثًا رقميًا" يُهدد مستقبل #الذكاء_الاصطناعي؟
منذ إطلاق #ChatGPT في 2022، يشبّه خبراء الذكاء الاصطناعي ما حدث بانفجار أول قنبلة ذرية!لماذا ؟
👇👇👇
#AI #ModelCollapse #DataQuality #ChatGPT #ArtificialIntelligence #Ethics #TechPolicy

tinyurl.com/5n9xhc6v

NewsFrontPage · الذكاء الاصطناعي في مواجهة التلوث الرقمي: سباق مع الزمن لحماية “البيانات النقية”الذكاء الاصطناعي يلوّث نفسه: هل نحن على حافة الانهيار الرقمي؟ منذ إطلاق ChatGPT في نوفمبر 2022، دخل العالم مرحلة جديدة من الذكاء الاصطناعي التوليدي. لكن مع كل هذا الحماس، بدأ بعض الباحثين يرفعون را…
Massimo Flore :verified:

👾 Model collapse: when AI systems trained on synthetic content begin degrading like photocopies of photocopies. Recent experiments show recursive training leads to semantic drift and loss of signal—errors accumulate slowly, precision decays steadily.

⚠️ The real risk isn't technical fragility but institutional misalignment. When knowledge production prioritizes scale over truth, collapse becomes symptom of concentrated power.
#AIResearch #ModelCollapse

🔗 open.substack.com/pub/massimof

Future Frontiers · How AI Is Transforming Our Search for InformationBy Massimo Flore
Tino Eberl

#KINews 🤪

Die unkontrollierte Nutzung von KI-generierten Inhalten beim #Training kann zu irreversiblen Fehlern in Modellen wie #LLMs führen, genannt „#ModelCollapse“. Dadurch verschwinden seltene Inhalte, was die Fairness der Vorhersagen beeinträchtigt. Die langfristige Erhaltung echter menschlicher #Datenquellen ist entscheidend. Um dies zu gewährleisten, sollte die Herkunft von Online-Inhalten nachverfolgt werden.

#KI #Datenqualität #Science #Technologie #AI

tino-eberl.de/ki-news/model-co

Tino Eberl · Model Collapse: Die Gefahr der KI-Überflutung mit maschinell generierten Inhalten
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Nemo_bis 🌈

@Aedius +1 sometimes called #ModelCollapse arxiv.org/abs/2305.17493 or #ModelAutophagyDisorder or #MatthewEffect
@HeavenlyPossum

arXiv.orgThe Curse of Recursion: Training on Generated Data Makes Models ForgetStable Diffusion revolutionised image creation from descriptive text. GPT-2, GPT-3(.5) and GPT-4 demonstrated astonishing performance across a variety of language tasks. ChatGPT introduced such language models to the general public. It is now clear that large language models (LLMs) are here to stay, and will bring about drastic change in the whole ecosystem of online text and images. In this paper we consider what the future might hold. What will happen to GPT-{n} once LLMs contribute much of the language found online? We find that use of model-generated content in training causes irreversible defects in the resulting models, where tails of the original content distribution disappear. We refer to this effect as Model Collapse and show that it can occur in Variational Autoencoders, Gaussian Mixture Models and LLMs. We build theoretical intuition behind the phenomenon and portray its ubiquity amongst all learned generative models. We demonstrate that it has to be taken seriously if we are to sustain the benefits of training from large-scale data scraped from the web. Indeed, the value of data collected about genuine human interactions with systems will be increasingly valuable in the presence of content generated by LLMs in data crawled from the Internet.
Miguel Afonso Caetano

#AI #GenerativeAI #LLMs #ModelCollapse #SyntheticData: "Stable diffusion revolutionized image creation from descriptive text. GPT-2 (ref. 1), GPT-3(.5) (ref. 2) and GPT-4 (ref. 3) demonstrated high performance across a variety of language tasks. ChatGPT introduced such language models to the public. It is now clear that generative artificial intelligence (AI) such as large language models (LLMs) is here to stay and will substantially change the ecosystem of online text and images. Here we consider what may happen to GPT-{n} once LLMs contribute much of the text found online. We find that indiscriminate use of model-generated content in training causes irreversible defects in the resulting models, in which tails of the original content distribution disappear. We refer to this effect as ‘model collapse’ and show that it can occur in LLMs as well as in variational autoencoders (VAEs) and Gaussian mixture models (GMMs). We build theoretical intuition behind the phenomenon and portray its ubiquity among all learned generative models. We demonstrate that it must be taken seriously if we are to sustain the benefits of training from large-scale data scraped from the web. Indeed, the value of data collected about genuine human interactions with systems will be increasingly valuable in the presence of LLM-generated content in data crawled from the Internet."

nature.com/articles/s41586-024

NatureAI models collapse when trained on recursively generated data - Nature Analysis shows that indiscriminately training generative artificial intelligence on real and generated content, usually done by scraping data from the Internet, can lead to a collapse in the ability of the models to generate diverse high-quality output.
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