Dynamically integrating new or rapidly evolving information after (Large) Language Model pre-training remains challenging, particularly in low-data scenarios or when dealing with private and specialized documents. In-context learning and retrieval-augmented generation (RAG) face limitations, including their high inference costs and their inability to capture global document information. In this paper, we propose a way of modularizing knowledge by training document-level Knowledge Modules (KMs). KMs are lightweight components implemented as parameter-efficient LoRA modules, which are trained to store information about new documents and can be easily plugged into models on demand. We show that next-token prediction performs poorly as the training objective for KMs. We instead propose Deep Context Distillation: we learn KMs parameters such as to simulate hidden states and logits of a teacher that takes the document in context. Our method outperforms standard next-token prediction and pre-instruction training techniques, across two datasets. Finally, we highlight synergies between KMs and RAG.
View on arXiv@article{caccia2025_2503.08727,
title={ Training Plug-n-Play Knowledge Modules with Deep Context Distillation },
author={ Lucas Caccia and Alan Ansell and Edoardo Ponti and Ivan Vulić and Alessandro Sordoni },
journal={arXiv preprint arXiv:2503.08727},
year={ 2025 }
}