Cross-Lingual Activation Steering for Multilingual Language Models
Cross-Lingual Activation Steering (CLAS) is a training-free method that adjusts neuron activations during inference to boost cross-lingual transfer in multilingual models. It works by categorizing neurons based on their activity across languages and applying targeted rescaling rules to selectively enhance shared features without changing model weights, leading to performance gains.