<ul data-eligibleForWebStory="true"><li>Large language models like GPT-3 are widely used, making it important to understand how they make decisions.</li><li>Researchers aim to use mathematical framework zigzag persistence to analyze the decision-making processes of these models.</li><li>Zigzag persistence is effective for dynamically characterizing data across model layers.</li><li>They introduce topological descriptors to measure the persistence and evolution of topological features throughout the layers.</li><li>Unlike other methods, their approach directly tracks the full evolutionary path of these features.</li><li>This framework provides insights into how prompts are rearranged and positions changed in the representation space.</li><li>The researchers demonstrate the framework's versatility by showing how it reacts to different models and datasets.</li><li>They showcase using zigzag persistence for layer pruning in a downstream task, achieving results similar to state-of-the-art methods.</li></ul>

Persistent Topological Features in Large Language Models

Discover more