Inject, Align, Recover: Staged Post-Training for Retrieval-Free Document Knowledge Internalization
Large language models often fail to answer questions about a bounded document collection when the source documents are not retrieved at inference time. We study this setting as document knowledge internalization: converting a fixed corpus into usable parametric knowledge for retrieval-free question answering. We propose IAR (Inject, Align, and Recover), a three-stage post-training framework that separates structured document knowledge injection, QA behavior alignment, and general ability recovery. Unlike conventional continued pretraining, Inject converts source documents into continuation, re
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- LinkedLinked via arxiv author · 85%Qian Kou →
“Inject, Align, Recover: Staged Post-Training for Retrieval-Free Document Knowledge Internalization”
- LinkedLinked via arxiv author · 85%Xiaofeng Shi →
“Inject, Align, Recover: Staged Post-Training for Retrieval-Free Document Knowledge Internalization”
- LinkedLinked via arxiv author · 85%Xiaosong Qiu →
“Inject, Align, Recover: Staged Post-Training for Retrieval-Free Document Knowledge Internalization”
- LinkedLinked via arxiv author · 85%Jinhua Zhou →
“Inject, Align, Recover: Staged Post-Training for Retrieval-Free Document Knowledge Internalization”