Navigating the Concept Space of Language Models

1. 1Train sparse autoencoders on large language model activations to generate thousands of features
2. 2Map these features to human-interpretable concepts
3. 3Use the mapped features to enable exploratory discovery of concepts at scale
4. 4Apply semantic search and other analysis techniques to individual features and concepts

This research benefits natural language processing (NLP) engineers and researchers who work with large language models, as it enables more efficient exploration and discovery of concepts within these models

💡 Sparse autoencoders can be used to map language model activations to human-interpretable concepts, enabling more efficient exploratory discovery

💡 Navigate concept space of language models with sparse autoencoders #LLMs #NLP