LLM Inference Enhanced by External Knowledge: A Survey
This work provides a comprehensive review for researchers and practitioners aiming to improve LLM inference, but it is incremental as it synthesizes existing methods rather than introducing new ones.
This survey systematically explores strategies for using external knowledge to enhance large language models (LLMs), addressing limitations like limited memory and hallucination by categorizing knowledge into unstructured and structured data, with a focus on tables and knowledge graphs, and analyzing trade-offs in interpretability, scalability, and performance.
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.