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News/PDF Retrieval Augmented Question Answering
arxiv
PublishedApril 7, 2026 at 4:00 AM
▲bullish

PDF Retrieval Augmented Question Answering

Source
arxiv.orgfull article ↗
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Publisher summary· verbatim

arXiv:2506.18027v2 Announce Type: replace Abstract: This paper presents an advancement in Question-Answering (QA) systems using a Retrieval Augmented Generation (RAG) framework to enhance information extraction from PDF files. Recognizing the richness and diversity of data within PDFs--including tex

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Discussion
Mentioned models
02
  • 01
    Retrieval Augmented Generation (RAG)
  • 02
    large language models
Source
↗
arxiv
Read original ↗All from arxiv →
Tags
04
#question-answering#multimodal#information-extraction#retrieval-augmented

No replies yet. Be first.

Mentioned models
02
  • 01
    Retrieval Augmented Generation (RAG)
  • 02
    large language models
Source
↗
arxiv
Read original ↗All from arxiv →
Tags
04
#question-answering#multimodal#information-extraction#retrieval-augmented

Related coverage

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arxivMODF-SIR: A Multi-agent Omni-modal Distilled Framework for Social Intelligence Reasoning20harxivPosition: Stop Anthropomorphizing Intermediate Tokens as Reasoning/Thinking Traces!20harxivARGUS: Stacked Multi-View Identity Mosaic Injection for Subject-Preserving Video Generation20harxivGeneralizing Beyond Suboptimality: Offline Reinforcement Learning Learns Effective Scheduling through Random Solutions20h
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