This perspective paper proposes the D-LLM framework, which couples the authors' GRAPHYP knowledge graphs with LLMs to personalize AI responses and make reasoning traceable, but no empirical validation is presented.
LLM Inference Enhanced by External Knowledge: A Survey
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abstract
Recent advancements in large language models (LLMs) have enhanced natural-language reasoning. However, their limited parametric memory and susceptibility to hallucination present persistent challenges for tasks requiring accurate, context-based inference. To overcome these limitations, an increasing number of studies have proposed leveraging external knowledge to enhance LLMs. This study offers a systematic exploration of strategies for using external knowledge to enhance LLMs, beginning with a taxonomy that categorizes external knowledge into unstructured and structured data. We then focus on structured knowledge, presenting distinct taxonomies for tables and knowledge graphs (KGs), detailing their integration paradigms with LLMs, and reviewing representative methods. Our comparative analysis further highlights the trade-offs among interpretability, scalability, and performance, providing insights for developing trustworthy and generalizable knowledge-enhanced LLMs.
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cs.AI 1years
2025 1verdicts
CONDITIONAL 1representative citing papers
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Matching Game Preferences Through Dialogical Large Language Models: A Perspective
This perspective paper proposes the D-LLM framework, which couples the authors' GRAPHYP knowledge graphs with LLMs to personalize AI responses and make reasoning traceable, but no empirical validation is presented.