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arXiv papers tackle long document retrieval with new chunking and hybrid strategies

By PulseAugur Editorial · [2 sources] ·

Two new arXiv papers explore advanced methods for improving retrieval from long documents. The first paper, "Calibrating Document-Local Boundaries for Contextual Chunk Retrieval," introduces a framework to adapt discriminative scaling and chunk-aware routing to enhance contextual chunk retrieval. The second paper, "Improving Contextual Retrieval for Long Documents using Semantic Chunking and Hybrid Retrieval Strategies," proposes a hybrid retrieval system that combines knowledge graphs with dense vector retrieval for more robust handling of complex, multi-hop queries. AI

IMPACT These papers introduce novel techniques for improving information retrieval from long documents, potentially enhancing the capabilities of AI systems that rely on processing extensive text data.

RANK_REASON Two academic papers published on arXiv detailing new methods for document retrieval.

Read on arXiv cs.CL →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

arXiv papers tackle long document retrieval with new chunking and hybrid strategies

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COVERAGE [2]

  1. arXiv cs.IR (Information Retrieval) TIER_1 English(EN) · Zhihua Chang ·

    Calibrating Document-Local Boundaries for Contextual Chunk Retrieval

    Late Chunking encodes an entire document before pooling at chunk boundaries, preserving document context in local retrieval units. Chunk-level post-training improves context-dependent retrieval, but shared context can make semantically similar same-document negatives difficult to…

  2. arXiv cs.CL TIER_1 English(EN) · Sakshi Garg, William B. Andreopoulos ·

    Improving Contextual Retrieval for Long Documents using Semantic Chunking and Hybrid Retrieval Strategies

    arXiv:2611.08498v1 Announce Type: cross Abstract: The vast amount of information available in lengthy, distributed documents makes finding relevant answers difficult, especially when the answer requires connecting evidence from multiple sources. Standard Retrieval-Augmented Gener…

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