DKL: Decoupled Knowledge Learning for Instruction-Tuned Language Models

arXiv:2609.02685 · cs.CL, cs.AI · Submitted 2026-09-02 · Read on arXiv

cs.CL, cs.AI

Submitted: 2026-09-02

Updated: 2026-09-02

Terminology

• RAG — 88× in this paper · explained in 3 episodes
• Embeddings — 32× in this paper · explained in 2 episodes
• Accuracy — 17× in this paper · explained in 1 episode
• Fine-Tuning — 11× in this paper · explained in 5 episodes
• Ablation — 10× in this paper · explained in 1 episode
• Large Language Model — 9× in this paper
• Benchmark — 8× in this paper · explained in 2 episodes
• LLM-as-a-Judge — 8× in this paper · explained in 3 episodes
• Hallucination — 6× in this paper · explained in 4 episodes
• Instruction Fine-Tuning — 6× in this paper
• Robustness — 6× in this paper · explained in 5 episodes
• Alignment — 5× in this paper · explained in 1 episode
• Extended Pre-Training — 5× in this paper
• Model Merging — 4× in this paper · explained in 2 episodes
• Adaptability — 3× in this paper · explained in 1 episode
• Adaptation — 3× in this paper · explained in 1 episode
• LLM judge — 3× in this paper · explained in 2 episodes
• Annotator Agreement — 2× in this paper · explained in 1 episode
• Catastrophic Forgetting — 2× in this paper · explained in 5 episodes
• Consistency — 2× in this paper · explained in 3 episodes
• Retrieval-Augmented Generation — 2× in this paper
• Synthetic Data Generation — 2× in this paper · explained in 5 episodes
• Tokenization — 2× in this paper · explained in 2 episodes
• Ablation study — 1× in this paper · explained in 1 episode
• Chain-of-Thought Prompting — 1× in this paper · explained in 2 episodes
• Distribution Shift — 1× in this paper · explained in 6 episodes
• Domain Adaptation — 1× in this paper · explained in 5 episodes
• Generalization — 1× in this paper · explained in 13 episodes
• Grounding — 1× in this paper · explained in 3 episodes
• In-Context Learning — 1× in this paper · explained in 4 episodes
• Instruction Tuning — 1× in this paper · explained in 2 episodes
• Large Language Model (LLM) — 1× in this paper · explained in 3 episodes
• Parameter-Efficient Fine-Tuning — 1× in this paper · explained in 2 episodes
• Retrieval-Augmented Generation (RAG) — 1× in this paper · explained in 16 episodes
• Transferability — 1× in this paper · explained in 4 episodes

Sources

• EcomGPT-CT: Continual Pre-training of E-commerce Large Language Models with Semi-structured Data
• Mistral 7B
• Demystifying Domain-adaptive Post-training for Financial LLMs
• Knowledge-Instruct: Effective Continual Pre-training from Limited Data using Instructions
• SuRe: Summarizing Retrievals using Answer Candidates for Open-domain QA of LLMs
• QuALITY: Question Answering with Long Input Texts, Yes!
• SMILE: Single-turn to Multi-turn Inclusive Language Expansion via ChatGPT for Mental Health Support
• Proximal Policy Optimization Algorithms
• BloombergGPT: A Large Language Model for Finance
• Instruction Tuning for Large Language Models: A Survey
• PIXIU: A Large Language Model, Instruction Data and Evaluation Benchmark for Finance
• PLLaMa: An Open-source Large Language Model for Plant Science
• Challenging BIG-Bench Tasks and Whether Chain-of-Thought Can Solve Them
• ChemLLM: A Chemical Large Language Model

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