MCompassRAG adds topic metadata to chunk representations and uses LLM distillation to train a lightweight topic-aware retriever, reporting 8.24% average information efficiency gain and over 5x lower latency than strong baselines across six benchmarks.
MoC: Mixtures of text chunking learners for retrieval-augmented generation system, in: Che, W., Nabende, J., Shutova, E., Pilehvar, M.T
3 Pith papers cite this work. Polarity classification is still indexing.
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SproutRAG introduces an attention-guided hierarchical framework that constructs a binary chunking tree for multi-granularity retrieval in RAG systems and reports a 6.1% average gain in information efficiency.
Empirical study claiming to be the first broad comparison of chunking methods in RAG, highlighting effectiveness, cost, and generalization limitations across scenarios.
citing papers explorer
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MCompassRAG: Topic Metadata as a Semantic Compass for Paragraph-Level Retrieval
MCompassRAG adds topic metadata to chunk representations and uses LLM distillation to train a lightweight topic-aware retriever, reporting 8.24% average information efficiency gain and over 5x lower latency than strong baselines across six benchmarks.
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SproutRAG: Attention-Guided Tree Search with Progressive Embeddings for Long-Document RAG
SproutRAG introduces an attention-guided hierarchical framework that constructs a binary chunking tree for multi-granularity retrieval in RAG systems and reports a 6.1% average gain in information efficiency.
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Chunking Methods on Retrieval-Augmented Generation - Effectiveness Evaluation Against Computational Cost and Limitations
Empirical study claiming to be the first broad comparison of chunking methods in RAG, highlighting effectiveness, cost, and generalization limitations across scenarios.