mlsanityData to 7 Oct 2026

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

arXiv 2312.07796 ↗First posted 12 Dec 2023cs.IR cs.AI cs.CLcited by 0

On the map: Retrieval-augmented generation → RAG pipeline design and evaluation

Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in RAG pipeline design and evaluation, one of 15 directions in Retrieval-augmented generation. Builds on 4 of its 18 references that are in the map. First posted on arXiv 12 Dec 2023.

Builds on

4 of 18 references.

References · 4

  1. 2020Language Models are Few-Shot Learnerscited by 32,199
  2. 2022Chain-of-Thought Prompting Elicits Reasoning in Large Language Modelscited by 12,302
  3. 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,160
  4. 2022ReAct: Synergizing Reasoning and Acting in Language Modelscited by 8,355

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  2. 2025A Survey on Knowledge-Oriented Retrieval-Augmented Generation
  3. 2024How Does Knowledge Selection Help Retrieval Augmented Generation?
  4. 2024A Comprehensive Survey of Retrieval-Augmented Generation (RAG): Evolution, Current Landscape and Future Directions
  5. 2025A Systematic Literature Review of Retrieval-Augmented Generation: Techniques, Metrics, and Challenges
  6. 2025Retrieval-Augmented Generation in Industry: An Interview Study on Use Cases, Requirements, Challenges, and Evaluation
  7. 2025A Systematic Review of Key Retrieval-Augmented Generation (RAG) Systems: Progress, Gaps, and Future Directions
  8. 2024Do You Know What You Are Talking About? Characterizing Query-Knowledge Relevance For Reliable Retrieval Augmented Generation