Open the Artificial Brain: Sparse Autoencoders for LLM Inspection | by Salvatore Raieli

|LLM|INTERPRETABILITY|SPARSE AUTOENCODERS|XAI|

A deep dive into LLM visualization and interpretation using sparse autoencoders

Explore the inner workings of Large Language Models (LLMs) beyond standard benchmarks. This article defines fundamental units within LLMs, discusses tools for analyzing complex interactions among layers and parameters, and explains how to visualize what these models learn, offering insights to correct unintended behaviors. — Image created by the author using DALL-E

All things are subject to interpretation whichever interpretation prevails at a given time is a function of power and not truth. — Friedrich Nietzsche

As AI systems grow in scale, it is increasingly difficult and pressing to understand their mechanisms. Today, there are discussions about the reasoning capabilities of models, potential biases, hallucinations, and other risks and limitations of Large Language Models (LLMs).

Open the Artificial Brain: Sparse Autoencoders for LLM Inspection | by Salvatore Raieli | Nov, 2024

|LLM|INTERPRETABILITY|SPARSE AUTOENCODERS|XAI|

A deep dive into LLM visualization and interpretation using sparse autoencoders