Neural networks are powerful tools that are trained on data rather than programmed with specific rules. However, despite our understanding of the math behind these networks, we still struggle to comprehend why they behave the way they do. This lack of understanding hinders our ability to identify and fix failures or ensure the safety of these models. Similarly, neuroscientists face challenges in understanding how the human brain translates thoughts and emotions into actions. Fortunately, studying artificial neural networks allows for easier experimentation and analysis. In a recent study, researchers discovered that analyzing individual neurons does not provide consistent insights into network behavior. Instead, they propose the use of “features,” which are patterns of neuron activation, to break down complex neural networks into more comprehensible parts. By studying these features, we can gain a better understanding of how these networks function. This research has the potential to enhance the interpretability of large language models and improve their safety and reliability.
https://www.anthropic.com/index/decomposing-language-models-into-understandable-components