DeepDecipher: Accessing and Investigating Neuron Activation in Large Language Models
This provides a practical tool for researchers, engineers, and developers to diagnose issues and audit LLMs, though it is incremental as it builds on existing interpretability methods.
The paper tackles the lack of accessible tools for interpreting large language models by introducing DeepDecipher, an API and interface for probing neurons in transformer MLP layers, which enables efficient and scalable analysis to enhance model transparency and trustworthiness.
As large language models (LLMs) become more capable, there is an urgent need for interpretable and transparent tools. Current methods are difficult to implement, and accessible tools to analyze model internals are lacking. To bridge this gap, we present DeepDecipher - an API and interface for probing neurons in transformer models' MLP layers. DeepDecipher makes the outputs of advanced interpretability techniques for LLMs readily available. The easy-to-use interface also makes inspecting these complex models more intuitive. This paper outlines DeepDecipher's design and capabilities. We demonstrate how to analyze neurons, compare models, and gain insights into model behavior. For example, we contrast DeepDecipher's functionality with similar tools like Neuroscope and OpenAI's Neuron Explainer. DeepDecipher enables efficient, scalable analysis of LLMs. By granting access to state-of-the-art interpretability methods, DeepDecipher makes LLMs more transparent, trustworthy, and safe. Researchers, engineers, and developers can quickly diagnose issues, audit systems, and advance the field.