Transformer AI幻觉; Fake AGI; 大语言模型 LLM are dead end

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论文 Hallucination Stations: On Some Basic Limitations of Transformer-Based Language Models

As Large Language Models (LLMs) become increasingly integrated into critical applications—from scientific research to financial systems—understanding their fundamental limitations becomes essential for safe deployment. "Hallucination Stations: On Some Basic Limitations of Transformer-Based Language Models" by Varin Sikka (Stanford University) and Vishal Sikka (VianAI Systems) provides a theoretical framework for understanding why LLMs generate factually incorrect or nonsensical outputs, commonly known as "hallucinations."
The authors apply computational complexity theory to demonstrate that certain limitations of transformer-based LLMs are not merely engineering challenges to be overcome through scaling or fine-tuning, but are inherent to the computational structure of these models. Their work offers a crucial counterpoint to the prevailing enthusiasm around LLM capabilities, particularly in the context of agentic AI systems that rely on LLMs for autonomous decision-making.

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