SKETCH , proposed in this paper, helps LLMs remember better by organizing knowledge with perfect memory.

SKETCH combines semantic text retrieval with knowledge graphs to enhance RAG systems, delivering more accurate and contextually rich responses across diverse datasets.

🤔 Original Problem:

→ Current RAG systems struggle with retrieving information from large datasets while maintaining context integrity. They often miss important connections when answers span multiple sections, leading to incomplete or inaccurate responses.

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🔧 Solution in this Paper:

→ SKETCH introduces semantic chunking that splits text into meaningful units preserving thematic integrity.

→ It builds knowledge graphs to capture entity relationships and enable multi-hop reasoning.

→ A hybrid retriever combines structured (knowledge graph) and unstructured (semantic) data for comprehensive information retrieval.

→ GPT-4 performs named entity recognition for query processing and entity extraction.

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💡 Key Insights:

→ Semantic chunking prevents context loss by maintaining thematic continuity

→ Knowledge graphs enable tracking relationships between distant pieces of information

→ Hybrid retrieval outperforms single-method approaches

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📊 Results:

→ Italian Cuisine Dataset: 94% answer relevancy, 99% context precision

→ QuALITY Dataset: 49% improvement over Naive RAG

→ QASPER Dataset: 100% better answer relevancy, 139% higher context precision

→ NarrativeQA Dataset: 525% improvement in answer relevancy