SproutRAG: Attention-Guided Tree Search with Progressive Embeddings for Long-Document RAG
Quick Answer
SproutRAG introduces an attention-guided hierarchical framework for retrieval-augmented generation, enhancing information efficiency by 6.1% over existing methods.
Quick Take
It organizes sentence-level chunks into coherent units without relying on external , enabling multi-granularity retrieval through a binary chunking tree. The framework is end-to-end trained, demonstrating superior performance across diverse benchmarks in scientific, legal, and open-domain contexts.
Key Points
- SproutRAG organizes sentence chunks into progressively larger coherent units.
- The framework improves information efficiency by 6.1% on average over strong baselines.
- It uses learned attention to capture semantic document structure effectively.
- Hierarchical beam search retrieves candidates at multiple granularities.
- Code is available on GitHub for further research and implementation.
Paper Resources
Source Excerpt
(RAG) systems must balance retrieval granularity with contextual coherence, a challenge that existing methods address through -guided chunking, single-level context expansion, or hierarchical summarization. These approaches variously depend on costly LLM calls during indexing or retrieval, limit context aggregation to a single granularity level, or introduce information loss through summarization. We present SproutRAG, an attention-guided hierarchical RAG framew
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