Get Started
Home
Topics
Search
Library
Research questionHow can instruction-tuned LLMs learn corpus-specific knowledge without exhaustive synthetic QA or instruction fine-tuning?Retrieval augmentation can fail when retrieved evidence is incorrect or incomplete. Updating model parameters can require extensive synthetic supervision or costly instruction fine-tuning, while also risking the model’s existing instruction-following ability.
AI
LLM Pretraining & Post-training
Natural Language Processing
Retrieval-Augmented Generation
Latest papersRecent research connected to this question, newest first.DKL: Decoupled Knowledge Learning for Instruction-Tuned Language ModelsApplies to instruction-tuned LLMs with corresponding base models, corpus-specific text knowledge, and RAG scenarios involving retrieval failures. The source describes extended pre-training, weight merging, and empirical RAG accuracy results, but does not establish broader deployment behavior.research paper · Sep 2, 2026
Related questions
How should low-resource LLM fine-tuning use task-level language priors with ambiguous or incomplete data?How can LLM agents generalize to unseen tasks without directly fine-tuning their policies?How can we translate instruction-tuning data without corrupting task constraints or required outputs?How can we construct accurate knowledge bases from an LLM’s parametric knowledge without retrieval, fine-tuning, or exceeding 32B parameters?