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CoCR-RAG: Enhancing Retrieval-Augmented Generation in Web Q&A via Concept-oriented Context Reconstruction

arXiv:2603.23989v1 Announce Type: new Abstract: Retrieval-augmented generation (RAG) has shown promising results in enhancing Q&A by incorporating information from the web and other external sources. However, the supporting documents retrieved from the heterogeneous web often originate from multiple sources with diverse writing styles, varying formats, and inconsistent granularity. Fusing such multi-source documents into a coherent and knowledge-intensive context remains a significant challenge, as the presence of irrelevant and redundant information can compromise the factual consistency of the inferred answers. This paper proposes the Concept-oriented Context Reconstruction RAG (CoCR-RAG), a framework that addresses the multi-source information fusion problem in RAG through linguistically grounded concept-level integration. Specifically, we introduce a concept distillation algorithm that extracts essential concepts from Abstract Meaning Representation (AMR), a stable semantic repres

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Kaize Shi, Xueyao Sun, Qika Lin, Firoj Alam, Qing Li, Xiaohui Tao, Guandong Xu · · 1 min read · 4 views
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arXiv:2603.23989v1 Announce Type: new Abstract: Retrieval-augmented generation (RAG) has shown promising results in enhancing Q&A by incorporating information from the web and other external sources. However, the supporting documents retrieved from the heterogeneous web often originate from multiple sources with diverse writing styles, varying formats, and inconsistent granularity. Fusing such multi-source documents into a coherent and knowledge-intensive context remains a significant challenge, as the presence of irrelevant and redundant information can compromise the factual consistency of the inferred answers. This paper proposes the Concept-oriented Context Reconstruction RAG (CoCR-RAG), a framework that addresses the multi-source information fusion problem in RAG through linguistically grounded concept-level integration. Specifically, we introduce a concept distillation algorithm that extracts essential concepts from Abstract Meaning Representation (AMR), a stable semantic representation that structures the meaning of texts as logical graphs. The distilled concepts from multiple retrieved documents are then fused and reconstructed into a unified, information-intensive context by Large Language Models, which supplement only the necessary sentence elements to highlight the core knowledge. Experiments on the PopQA and EntityQuestions datasets demonstrate that CoCR-RAG significantly outperforms existing context-reconstruction methods across these Web Q&A benchmarks. Furthermore, CoCR-RAG shows robustness across various backbone LLMs, establishing itself as a flexible, plug-and-play component adaptable to different RAG frameworks.

Executive Summary

The article proposes CoCR-RAG, a framework for enhancing retrieval-augmented generation in web Q&A through concept-oriented context reconstruction. By leveraging linguistically grounded concept-level integration, CoCR-RAG addresses the challenge of fusing multi-source documents into a coherent and knowledge-intensive context. The proposed framework introduces a concept distillation algorithm that extracts essential concepts from Abstract Meaning Representation (AMR) and reconstructs them into a unified context using Large Language Models. Experimental results on PopQA and EntityQuestions datasets demonstrate CoCR-RAG's superiority over existing context-reconstruction methods. The framework's flexibility and adaptability to different RAG frameworks are also highlighted. CoCR-RAG's ability to reconstruct meaningful contexts from diverse web documents shows promise for enhancing web Q&A systems, but its limitations in handling extremely large or heterogeneous datasets require further investigation.

Key Points

  • ▸ CoCR-RAG addresses the challenge of fusing multi-source documents into a coherent context.
  • ▸ The framework leverages linguistically grounded concept-level integration for context reconstruction.
  • ▸ CoCR-RAG outperforms existing context-reconstruction methods on PopQA and EntityQuestions datasets.

Merits

Strength in Concept-Level Integration

CoCR-RAG's concept distillation algorithm and concept-level integration enable the framework to reconstruct meaningful contexts from diverse web documents, demonstrating its potential for enhancing web Q&A systems.

Flexibility and Adaptability

CoCR-RAG's ability to adapt to different RAG frameworks and backbone LLMs highlights its flexibility and practicality for real-world applications.

Improved Performance

Experimental results show that CoCR-RAG outperforms existing context-reconstruction methods, demonstrating its effectiveness in addressing the multi-source information fusion problem in RAG.

Demerits

Limitation in Handling Large Datasets

The framework may struggle with extremely large or heterogeneous datasets, requiring additional development to address these challenges.

Dependence on Large Language Models

CoCR-RAG's reliance on Large Language Models may limit its applicability in situations where these models are not readily available or are less accurate.

Expert Commentary

The article presents a well-structured framework for addressing the challenge of fusing multi-source documents in retrieval-augmented generation. CoCR-RAG's reliance on linguistically grounded concept-level integration and its adaptability to different RAG frameworks demonstrate its potential for practical applications. However, its limitations in handling large datasets and dependence on Large Language Models require further investigation. The development of CoCR-RAG highlights the importance of addressing the multi-source information fusion problem in RAG, and its implications for enhancing web Q&A systems and AI-powered tools are significant.

Recommendations

  • ✓ Further investigation into CoCR-RAG's performance on extremely large or heterogeneous datasets is necessary to ensure its practical applicability.
  • ✓ Developing techniques to improve the accuracy and availability of Large Language Models would enhance CoCR-RAG's effectiveness and broaden its applicability.

Sources

Original: arXiv - cs.CL

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