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cs.CL2024
StructRAG: Boosting Knowledge Intensive Reasoning of LLMs via Inference-time Hybrid Information Structurization
Zhuoqun Li, Xuanang Chen, Haiyang Yu +7
Retrieval-augmented generation (RAG) is a key means to effectively enhance large language models (LLMs) in many knowledge-based tasks. However, existing RAG methods struggle with k…
cs.CL2024
Seg2Act: Global Context-aware Action Generation for Document Logical Structuring
Zichao Li, Shaojie He, Meng Liao +6
Document logical structuring aims to extract the underlying hierarchical structure of documents, which is crucial for document intelligence. Traditional approaches often fall short…
cs.CL2024
READoc: A Unified Benchmark for Realistic Document Structured Extraction
Zichao Li, Aizier Abulaiti, Yaojie Lu +5
Document Structured Extraction (DSE) aims to extract structured content from raw documents. Despite the emergence of numerous DSE systems, their unified evaluation remains inadequa…