LLM self-learning doomed to inevitable model collapse, research suggests

A recent paper by Hector Zenil argues that large language models (LLMs) are inherently prone to model collapse when attempting to self-learn. The paper posits that LLMs, as statistical models, will converge on a statistical singularity rather than achieving artificial general intelligence if they rely solely on their own outputs for training. Continuous training with external, human-generated data is necessary to prevent this degradation and maintain model performance. AI

IMPACT Highlights the critical need for external data to prevent LLM degradation and maintain performance.

RANK_REASON Academic paper detailing a theoretical risk of self-training in LLMs.

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• AGI
• Hector Zenil
• LLM
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• safety

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