SKETCH: Structured Knowledge Enhanced Text Comprehension for Holistic Retrieval (2412.15443v1)

Abstract: Retrieval-Augmented Generation (RAG) systems have become pivotal in leveraging vast corpora to generate informed and contextually relevant responses, notably reducing hallucinations in LLMs. Despite significant advancements, these systems struggle to efficiently process and retrieve information from large datasets while maintaining a comprehensive understanding of the context. This paper introduces SKETCH, a novel methodology that enhances the RAG retrieval process by integrating semantic text retrieval with knowledge graphs, thereby merging structured and unstructured data for a more holistic comprehension. SKETCH, demonstrates substantial improvements in retrieval performance and maintains superior context integrity compared to traditional methods. Evaluated across four diverse datasets: QuALITY, QASPER, NarrativeQA, and Italian Cuisine-SKETCH consistently outperforms baseline approaches on key RAGAS metrics such as answer_relevancy, faithfulness, context_precision and context_recall. Notably, on the Italian Cuisine dataset, SKETCH achieved an answer relevancy of 0.94 and a context precision of 0.99, representing the highest performance across all evaluated metrics. These results highlight SKETCH's capability in delivering more accurate and contextually relevant responses, setting new benchmarks for future retrieval systems.

• The paper introduces SKETCH, which combines semantic chunking with knowledge graphs to improve text comprehension in retrieval-augmented generation systems.
• It demonstrates significant performance gains across datasets, achieving metrics such as a 0.94 answer relevancy and 0.99 context precision on the Italian Cuisine dataset.
• The study paves the way for scalable integration of structured and unstructured data, offering promising directions for advanced natural language processing applications.

Structured Knowledge Enhanced Text Comprehension in Retrieval-Augmented Generation

The paper "SKETCH: Structured Knowledge Enhanced Text Comprehension for Holistic Retrieval" introduces an advanced methodology aimed at overcoming prevalent limitations in Retrieval-Augmented Generation (RAG) systems. RAG systems have seen significant use in mitigating hallucination issues within LLMs by enabling factually updated responses. However, these systems struggle with processing efficiently and retrieving information from extensive datasets, often hindering their ability to provide comprehensive context understanding. Thus, the SKETCH methodology proposes an innovative fusion of semantic text retrieval and knowledge graphs to enhance the retrieval process by leveraging both structured and unstructured data.

SKETCH Methodology

The SKETCH model is designed to integrate the advantages of semantic chunking with the organized representation of knowledge graphs. Semantic chunking ensures the division of text into semantically coherent units, thus preserving context integrity essential for effective retrieval. This approach stands in contrast to traditional chunking methods that may disrupt thematic flow. Knowledge graphs, on the other hand, offer a structured depiction of entities and relationships, facilitating a richer understanding of context, which is particularly beneficial for complex queries demanding multi-hop reasoning.

Strong Numerical Results

The efficacy of SKETCH is demonstrated through evaluation across four datasets: QuALITY, QASPER, NarrativeQA, and an Italian Cuisine dataset. The results consistently show that SKETCH outperforms baseline methods across key RAGAS metrics, including answer relevancy, faithfulness, context precision, and context recall. On the Italian Cuisine dataset, SKETCH achieved an answer relevancy score of 0.94 and context precision of 0.99, outshining other tested methods. These results underscore the model's capability to provide accurate and contextually pertinent responses across diverse domains.

Implications and Future Directions

The fusion of semantic chunking and knowledge graphs within the SKETCH framework holds significant implications for both practical applications and future theoretical advancements in AI. Practically, SKETCH enhances the reliability and precision of information retrieval in large-scale datasets, which is critical for applications in natural language processing tasks, including question answering and document comprehension. Theoretically, SKETCH introduces a robust framework that challenges traditional models, suggesting new avenues for refining retrieval processes that could further bolster the efficacy of RAG systems.

Potential future developments could focus on optimizing the scalability and efficiency of knowledge graph construction, addressing existing limitations like the labor-intensive nature of building large-scale graphs. Another area for exploration involves improving metrics such as context recall and faithfulness, as SKETCH's performance, though superior, indicates room for refinement in synthesizing and preserving the intricacies of complex queries.

Conclusion

In conclusion, the introduction of SKETCH signals a considerable step forward in the evolution of RAG systems by effectively merging structured and unstructured data for enhanced text comprehension and retrieval accuracy. While challenges remain, particularly concerning the construction and integration of large-scale knowledge graphs and the computational costs associated with such endeavors, the robustness and flexibility of the SKETCH methodology present a promising pathway for future innovations in the field.