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2025: A GPT Odyssey. Deconstructing Intelligence by Gradual Dissolution of a Transformer

Hierarchical processing and information granulation have proven essential for intelligent systems, as exemplified by Large Language Models (LLMs) backed by Transformer architectures, which leverage stacked attention modules to learn progressively richer semantic features. In this work, we offer an investigation of the role of attention layers in the hierarchy through a GPT-2 layer ablation methodology, which recalls the deactivation of the HAL 9000 computer modules in the iconic scene of the ...

LSTM in Recursive Feedback Loops: A Study on Textual Evolution and Complexity

The current worldwide use of generative models in the field of Artificial Intelligence creates new possibilities but also new challenges and problems. In such an interconnected world, data produced by machines can become part of the training phases of new Language Models (LMs), and this can result in a problem that some scholars are starting to call "model collapse". In this paper, we discuss the problem from three points of view. Firstly, we show how the accumulation of data can cause a give...

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