arXiv:2606.03247v1 Announce Type: new Abstract: Document-grounded dialogue systems (DGDS) utilize knowledge from external documents to answer domain-specific user questions. Existing solutions typically divide documents into independent passages for retrieval and response generation. This approach, however, neither makes good use of structural information within documents nor provides enough (document) context for knowledge selection and responses. This paper proposes SF-Re2G to address such issues systematically. Firstly, we seek to improve a passage representation by contrasting it with othe
The rapid advancement in RAG architectures for large language models is driving continuous innovation in how knowledge is retrieved and utilized, with current methods often proving insufficient for complex tasks.
Improving the efficiency and accuracy of document-grounded dialogue systems directly enhances the practical utility and reliability of AI applications, particularly those requiring nuanced understanding of large knowledge bases.
The focus shifts from simple passage-based retrieval to methods that leverage structural information within documents, leading to more contextually aware and accurate AI responses.
- · AI developers
- · Enterprises deploying RAG systems
- · Users of AI-powered search and dialogue tools
- · Basic RAG systems without structural understanding
- · Knowledge management systems reliant on keyword search alone
AI systems will exhibit improved understanding and response generation capabilities when dealing with complex, structured documents.
This could lead to more reliable and trustworthy AI applications in critical domains like legal, medical, and technical support.
The enhanced ability of AI to process and synthesize structured information might accelerate the automation of knowledge work, reducing demand for human synthesis tasks.
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