Steering Language Models Before They Speak: Logit-Level Interventions
SWAI introduces a training-free method to steer language model outputs during inference by modifying logits based on corpus statistics. It precomputes token scores from labeled data to quantify how much each word relates to a desired characteristic, then applies these scores as a bias during text generation. This allows users to control output traits like reading level or politeness without retraining the model.