Gemma-2-9B

Researchers propose an empirical test for error correction in large language models, finding that residual-stream activations are robust to small perturbations and that feature-specific directions are…

Researchers at an undisclosed institution analyzed LoRA fine-tuning in Gemma-2-9B using sparse autoencoders, finding that adapter-specific feature dictionaries show weak geometric alignment with pretr…

Researchers have developed a principled method for multilingual language steering in large language models using sparse autoencoders (SAEs), addressing the unreliability of existing English-only SAE a…