Papers › Harnessing Retrieval-Augmented Generation (RAG) for Uncovering Knowledge Gaps

Harnessing Retrieval-Augmented Generation (RAG) for Uncovering Knowledge Gaps

12 Dec 2023arXiv:2312.07796archive 2025-07-28

Joan Figuerola Hurtado

The paper presents a methodology for uncovering knowledge gaps on the internet using the Retrieval Augmented Generation (RAG) model. By simulating user search behaviour, the RAG system identifies and addresses gaps in information retrieval systems. The study demonstrates the effectiveness of the RAG system in generating relevant suggestions with a consistent accuracy of 93%. The methodology can be applied in various fields such as scientific discovery, educational enhancement, research development, market analysis, search engine optimisation, and content development. The results highlight the value of identifying and understanding knowledge gaps to guide future endeavours.

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webeng/llm_knowledge_gap_finder officialmentioned in papermentioned on GitHub report

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Tasks

Information Retrieval RAG Retrieval Retrieval-augmented Generation scientific discovery

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Methods

Adam Attention Attention Dropout BART BERT BPE Dense Connections Dropout Layer Normalization Linear Layer Linear Warmup With Linear Decay Multi-Head Attention RAG Residual Connection Softmax Weight Decay WordPiece

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