Multi-layered embeddings unlock complex legal knowledge by preserving document hierarchies
This paper shows how hierarchical embedding system makes legal document retrieval smarter and more precise.
https://arxiv.org/abs/2411.07739
🔍 Original Problem:
Traditional keyword searches fail to capture legal document hierarchies and semantic relationships. Legal texts have complex structures - from individual clauses to entire documents - making it hard for LLMs to understand and retrieve relevant information accurately.
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🛠️ Solution in this Paper:
→ Creates embeddings at multiple granularity levels - document, component, hierarchy, unit, and enumeration levels.
→ Each article (basic unit) gets its own embedding to capture specific legal provisions.
→ Broader groups like chapters and titles get embeddings to represent thematic relationships.
→ Uses cosine similarity with a 25% threshold to match query intent.
→ Implements textual boundary filtering to avoid content overlap.
→ Sets 2500 token baseline count for manageable responses.
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💡 Key Insights:
→ Legal texts need hierarchical representation beyond simple semantic chunking
→ Context preservation is crucial - embedding items with their parent context
→ Multi-layered approach enables flexible retrieval based on query specificity
→ Method applies beyond legal domain to any hierarchically structured text
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📊 Results:
→ Generated 32 detailed chunks compared to traditional 4-chunk approach
→ Improved semantic representation across document hierarchy levels
→ Enhanced retrieval precision through contextual embedding
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