Large language models can do jaw-dropping things. But nobody knows exactly why.

That's a succinct way to describe the current state of understanding regarding large language models. While they have achieved remarkable results across various tasks, the exact mechanisms that enable their performance are not fully understood. Here are some key points to consider:

1. Architecture and Training: The architecture of models like GPT-4 is based on deep learning, specifically transformer networks. These models are trained on massive datasets using unsupervised learning techniques, learning to predict the next word in a sequence. The scale of data and the number of parameters (billions or even trillions) contribute significantly to their capabilities.
2. Emergent Behaviors: As these models grow in size and complexity, they exhibit emergent behaviors that were not explicitly programmed. These behaviors include sophisticated language understanding, translation, summarization, and even some reasoning abilities.
3. Interpretability: Despite their impressive performance, interpreting why these models make certain decisions remains a challenge. The models operate as black boxes, with their internal representations and learned patterns being difficult to analyze and understand.
4. Research Efforts: There is ongoing research aimed at improving the interpretability and transparency of large language models. Techniques such as attention visualization, probing tasks, and model distillation are being explored to gain better insights into how these models function.
5. Ethical and Safety Concerns: The lack of understanding of these models' inner workings raises ethical and safety concerns. It becomes challenging to predict and control their behavior, making it crucial to develop robust guidelines and safety measures.

Overall, while the exact reasons behind the performance of large language models remain elusive, continuous research is shedding light on their capabilities and limitations.