Jong-hun Shin

From Continuous Pretraining to Domain-Adaptive Reranking via Task Vector Adaptation

This work proposes a fine-grained task vector adaptation method that learns parameter-wise scaling coefficients for the task vector that are optimized using a language modeling objective while keeping all model parameters fixed, enabling effective integration of domain-specific knowledge without degrading reranking capabilities.