Sparse Autoencoders Can Capture Language-Specific Concepts Across Diverse Languages
Researchers used Sparse Autoencoders (SAEs) to find specific, language-related features inside Large Language Models (LLMs). They developed SAE-LAPE to isolate these features, finding they are mainly in later layers. These features control how the model generates text in different languages and can be used for accurate language identification.