<ul><li>Large Language Models (LLMs) are becoming increasingly large, limiting their use in computationally constrained environments.</li><li>Researchers have proposed a novel approach to extract task-specific circuits from LLMs for faster inference.</li><li>The extracted subset of the LLM can perform a targeted task without additional training and with a small amount of data samples.</li><li>The resulting models are considerably smaller, reducing the number of parameters up to 82.77% and more interpretable using Mechanistic Interpretability (MI) techniques.</li></ul>

Extracting Interpretable Task-Specific Circuits from Large Language Models for Faster Inference

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