Junghun Yuk

Distribution-aware Language Neuron selection is proposed, which leverages pairwise relationships between per-language activation distributions over the full activation range, including negative values, to quantify each neuron's language specificity by clustering languages using pairwise overlap coefficients between their activation distributions.

Benign synthetic data can covertly inject targeted biases into LLMs, posing a hidden threat to model alignment and safety.