SymbioticRAG: Enhancing Document Intelligence Through Human-LLM Symbiotic Collaboration (2505.02418v1)

Abstract: We present \textbf{SymbioticRAG}, a novel framework that fundamentally reimagines Retrieval-Augmented Generation~(RAG) systems by establishing a bidirectional learning relationship between humans and machines. Our approach addresses two critical challenges in current RAG systems: the inherently human-centered nature of relevance determination and users' progression from "unconscious incompetence" in query formulation. SymbioticRAG introduces a two-tier solution where Level 1 enables direct human curation of retrieved content through interactive source document exploration, while Level 2 aims to build personalized retrieval models based on captured user interactions. We implement Level 1 through three key components: (1)~a comprehensive document processing pipeline with specialized models for layout detection, OCR, and extraction of tables, formulas, and figures; (2)~an extensible retriever module supporting multiple retrieval strategies; and (3)~an interactive interface that facilitates both user engagement and interaction data logging. We experiment Level 2 implementation via a retriever strategy incorporated LLM summarized user intention from user interaction logs. To maintain high-quality data preparation, we develop a human-on-the-loop validation interface that improves pipeline output while advancing research in specialized extraction tasks. Evaluation across three scenarios (literature review, geological exploration, and education) demonstrates significant improvements in retrieval relevance and user satisfaction compared to traditional RAG approaches. To facilitate broader research and further advancement of SymbioticRAG Level 2 implementation, we will make our system openly accessible to the research community.

An Expert Overview of SymbioticRAG: Enhancing Document Intelligence Through Human-LLM Symbiotic Collaboration

SymbioticRAG introduces a novel framework for Retrieval-Augmented Generation (RAG) systems, aiming to establish a bidirectional learning relationship between humans and LLMs. This approach tackles two critical obstacles prevalent in existing RAG systems: the necessity of human-centric relevance determination and users' initial stage of "unconscious incompetence" when formulating queries. The system is devised to bolster retrieval relevance and user satisfaction, demonstrated effectively across scenarios such as literature review, geological exploration, and education.

Key Innovations and Implementation

SymbioticRAG unfolds in two levels: Level 1 facilitates direct human involvement in curating retrieved content using interactive source document exploration. This tier includes a comprehensive document processing pipeline tailored for layout detection, optical character recognition (OCR), and extraction of tables, formulas, and figures. It utilizes a flexible retriever module supporting diverse retrieval strategies and an interactive user interface for engagement and interaction data logging. Level 2 progresses toward personalized retrieval models based on compiled user interactions, employing an LLM to summarize user intentions from interaction logs.

The document processing pipeline effectively handles varied document formats by converting them into PDFs and processing page images through specialized models for different content types. This layout-aware transformation enables granular retrieval over embedded layout block chunks. Human-on-the-loop validation interfaces further refine processing outputs, leveraging human expertise to ensure high-quality data for specialized extraction tasks, thus advancing domain-specific research.

The retriever component exhibits adaptability, accommodating semantic similarity-based retrieval methods and more advanced strategies as they emerge. Specifically, SymbioticRAG enables multi-round, context-aware document interactions, contrasting with conventional one-directional LLM-based systems. This bidirectional interaction empowers users, allowing them to refine retrieval, explore unknown domains, and gradually form comprehensive domain knowledge.

Quantitative Evaluation and Insights

SymbioticRAG’s evaluation reveals significant improvements in retrieval relevance and user satisfaction compared to traditional RAG systems. The human-retriever distance score illustrates enhanced alignment with human selection, indicating that the system effectively captures user semantic intent. User satisfaction ratings highlight the user-centered design's effectiveness in addressing complex information-seeking tasks.

Use case analysis outlines users' progression from "unconscious incompetence" to "conscious incompetence", underscoring the system's capability to foster knowledge discovery. This marks a significant advance in interactive search experiences, echoing the foundational vision of human-machine symbiotic interaction.

Implications for Future Research and Development

SymbioticRAG represents an important stride in aligning artificial intelligence with human-centric goals, and its implications are multifaceted. Theoretically, it advocates for redefining RAG systems by integrating deep learning models with direct human inputs, paving the way for advanced retrieval intelligence adaptable to individual user behavior. Practically, it suggests avenues for developing personalized retrieval models, enhancing real-time interaction and decision-making processes across domains.

Future developments with Level 2 implementation promise further integration of user feedback, positioning AI systems as personalized assistants capable of evolving alongside individual needs. As LLMs continue to advance, the symbiotic collaboration framework outlined by SymbioticRAG provides a fundamental model for exploring domain-specific information spaces and learner-centric problem solving, contributing to the expansion of AI's practical capabilities.