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Large Language Models (LLMs) have experienced a rapid rise in AI, changing awide range of applications with their advanced capabilities. As these modelsbecome increasingly integral to decision-making, the need for thoroughinterpretability has never been more critical. Mechanistic Interpretabilityoffers a pathway to this understanding by identifying and analyzing specificsub-networks or 'circuits' within these complex systems. A crucial aspect ofthis approach is Automated Circuit Discovery, which facilitates the study oflarge models like GPT4 or LLAMA in a feasible manner. In this context, ourresearch evaluates a recent method, Brain-Inspired Modular Training (BIMT),designed to enhance the interpretability of neural networks. We demonstrate howBIMT significantly improves the efficiency and quality of Automated CircuitDiscovery, overcoming the limitations of manual methods. Our comparativeanalysis further reveals that BIMT outperforms existing models in terms ofcircuit quality, discovery time, and sparsity. Additionally, we provide acomprehensive computational analysis of BIMT, including aspects such astraining duration, memory allocation requirements, and inference speed. Thisstudy advances the larger objective of creating trustworthy and transparent AIsystems in addition to demonstrating how well BIMT works to make neuralnetworks easier to understand.

Evaluating Brain-Inspired Modular Training in Automated Circuit Discovery for Mechanistic Interpretability