<ul><li>Understanding internal representations of large language models is crucial for interpretability research.</li><li>A new framework called InverseScope is introduced for interpreting neural activations through input inversion.</li><li>InverseScope defines a distribution over inputs to generate similar activations and analyze to infer encoded features.</li><li>It scales inversion-based interpretability methods for larger models and enables quantitative analysis of internal representations in real-world LLMs.</li></ul>

InverseScope: Scalable Activation Inversion for Interpreting Large Language Models

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