Mechanistic Interpretability: Peeking Inside an LLM

What’s Happening

Alright so Are the human-like cognitive abilities of LLMs real or fake?

How does information travel through the neural network? Is there hidden knowledge inside an LLM? (let that sink in)

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The Details

Intro Let’s discuss how to examine and manipulate an LLM’s neural network. This is the topic of mechanistic interpretability research, and it can answer many exciting questions.

Remember: An LLM is a deep artificial neural network, made up of neurons and weights that determine how strongly those neurons are connected. What makes a neural network arrive at its conclusion?

Why This Matters

How much of the information it processes does it consider and analyze adequately? These sorts of questions have been investigated in a vast number of publications at least since deep neural networks kicked off showing promise. To be clear, mechanistic interpretability existed before LLMs did, and was already an exciting aspect of Explainable AI research with earlier deep neural networks.

This adds to the ongoing AI race that’s captivating the tech world.

The Bottom Line

So, this section is a quick reminder of the components of an LLM. LLMs use a sequence of input tokens to predict the next token.

What’s your take on this whole situation?