What question did this study set out to answer?

This survey aims to investigate approaches for integrating external knowledge into large language models (LLMs) to improve their performance in knowledge-intensive tasks.

June 20, 2026Semantic WebOpen Access

External Knowledge Integration in Large Language Models: A Survey on Methods, Challenges, and Future Directions

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Authors

IYItisha YadavDeutsches Zentrum für Luft- und Raumfahrt e. V. (DLR)SSSirko SchindlerDeutsches Zentrum für Luft- und Raumfahrt e. V. (DLR)DPDiana PetersDeutsches Zentrum für Luft- und Raumfahrt e. V. (DLR)

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Implication

Survey explores methods for integrating external knowledge into LLMs, enhancing accuracy and effectiveness in specialized domains.

Key Points

This survey aims to investigate approaches for integrating external knowledge into large language models (LLMs) to improve their performance in knowledge-intensive tasks.
Analyzed parametric and non-parametric approaches for knowledge integration in LLMs.
Focused on decoder-based, autoregressive models used in natural language understanding.
Discussed implications for software developers and NLP researchers in specialized domains.
Identified limitations of LLMs, including hallucinations and contextual knowledge gaps.
Highlighted potential improvements in reasoning capabilities and factual accuracy through external knowledge integration.
Provided foundational insights for future research in enhancing LLM-based NLU systems.

Cite This Study

Yadav et al. (2026) studied this question.

synapsesocial.com/papers/6a3632d2db0793dc1a5395c1 https://doi.org/10.1177/22104968261453132

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External Knowledge Integration in Large Language Models: A Survey on Methods, Challenges, and Future Directions

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Key Points

Cite This Study

External Knowledge Integration in Large Language Models: A Survey on Methods, Challenges, and Future Directions

Discussion

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Implication

Key Points

Cite This Study

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