Bridging Semantic Gaps in RAG through Generated Context Knowledge Fusion

arXiv:2609.37171 · cs.CL · Submitted 2026-09-29 · Read on arXiv

cs.CL

Submitted: 2026-09-29

Updated: 2026-09-29

Terminology

• Question Answering — 15× in this paper
• Accuracy — 10× in this paper · explained in 1 episode
• Knowledge-Aware Semantic Bridging — 10× in this paper
• RAG — 9× in this paper · explained in 3 episodes
• Exact Match — 8× in this paper
• Alignment — 5× in this paper · explained in 1 episode
• Direct Preference Optimization — 4× in this paper
• Fine-Tuning — 3× in this paper · explained in 5 episodes
• Hallucination — 3× in this paper · explained in 4 episodes
• Retrieval-Augmented Generation — 3× in this paper
• Abstraction — 2× in this paper · explained in 1 episode
• Adaptive Selection — 2× in this paper · explained in 1 episode
• Embeddings — 2× in this paper · explained in 2 episodes
• Generative Model — 2× in this paper · explained in 2 episodes
• Reinforcement Learning — 2× in this paper · explained in 4 episodes
• Semantic Gap — 2× in this paper · explained in 2 episodes
• Ablation — 1× in this paper · explained in 1 episode
• Adaptive Strategy — 1× in this paper · explained in 1 episode
• Benchmark — 1× in this paper · explained in 2 episodes
• Computational overhead — 1× in this paper · explained in 2 episodes
• Contrastive Learning — 1× in this paper · explained in 5 episodes
• Core — 1× in this paper · explained in 2 episodes
• Direct Preference Optimization (DPO) — 1× in this paper · explained in 9 episodes
• Generalization — 1× in this paper · explained in 13 episodes
• Generative Models — 1× in this paper · explained in 3 episodes
• Large Language Models (LLMs) — 1× in this paper · explained in 6 episodes
• Retrieval-Augmented Generation (RAG) — 1× in this paper · explained in 16 episodes
• Semantic Alignment — 1× in this paper · explained in 3 episodes
• Semantic Similarity — 1× in this paper · explained in 2 episodes

Sources

• Unsupervised Dense Information Retrieval with Contrastive Learning
• Modeling Uncertainty and Using Post-fusion as Fallback Improves Retrieval Augmented Generation with LLMs

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