mlsanityData to 14 Oct 2026

LLM Inference Enhanced by External Knowledge: A Survey

arXiv 2505.24377 ↗First posted 30 May 2025cs.CLcited by 3

On the map: LLM reasoning → Reasoning benchmarks and evaluation

Abstract (arXiv)

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.

Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Placed in Reasoning benchmarks and evaluation, one of 17 directions in LLM reasoning. Builds on 31 of its 46 references that are in the map. First posted on arXiv 30 May 2025.

Builds on

31 of 46 references.

Closest ancestors · 2

Same direction, newest first

  1. 2023StructGPT: A General Framework for Large Language Model to Reason over Structured Data16 May
  2. 2023LLaMA: Open and Efficient Foundation Language Models27 Feb

Other references · 29

  1. 2020Language Models are Few-Shot Learnerscited by 32,419
  2. 2023GPT-4 Technical Reportcited by 18,396
  3. 2024The Llama 3 Herd of Modelscited by 14,715
  4. 2018HotpotQA: A Dataset for Diverse, Explainable Multi-hop Question Answeringcited by 3,754
  5. 2017TriviaQA: A Large Scale Distantly Supervised Challenge Dataset for Reading Comprehensioncited by 2,955
  6. 2023Retrieval-Augmented Generation for Large Language Models: A Surveycited by 2,212
  7. 2017Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learningcited by 687
  8. 2023Unifying Large Language Models and Knowledge Graphs: A Roadmapcited by 668
  9. 2020Knowledge Graphscited by 621
  10. 2015Compositional Semantic Parsing on Semi-Structured Tablescited by 515
  11. 2019TabFact: A Large-scale Dataset for Table-based Fact Verificationcited by 428
  12. 2018The Web as a Knowledge-base for Answering Complex Questionscited by 410
17 more
  1. 2023Think-on-Graph: Deep and Responsible Reasoning of Large Language Model on Knowledge Graphcited by 238
  2. 2022Binding Language Models in Symbolic Languagescited by 228
  3. 2024Chain-of-Table: Evolving Tables in the Reasoning Chain for Table Understandingcited by 188
  4. 2023Large Language Models are Versatile Decomposers: Decompose Evidence and Questions for Table-based Reasoningcited by 178
  5. 2021FeTaQA: Free-form Table Question Answeringcited by 175
  6. 2023Knowledge-Augmented Language Model Prompting for Zero-Shot Knowledge Graph Question Answeringcited by 119
  7. 2024Plan-on-Graph: Self-Correcting Adaptive Planning of Large Language Model on Knowledge Graphscited by 78
  8. 2023ReAcTable: Enhancing ReAct for Table Question Answeringcited by 76
  9. 2023Retrieve-Rewrite-Answer: A KG-to-Text Enhanced LLMs Framework for Knowledge Graph Question Answeringcited by 65
  10. 2024Generate-on-Graph: Treat LLM as both Agent and KG in Incomplete Knowledge Graph Question Answeringcited by 60
  11. 2024Large Language Model for Table Processing: A Surveycited by 52
  12. 2024TabSQLify: Enhancing Reasoning Capabilities of LLMs Through Table Decompositioncited by 39
  13. 2024ProTrix: Building Models for Planning and Reasoning over Tables with Sentence Contextcited by 23
  14. 2024H-STAR: LLM-driven Hybrid SQL-Text Adaptive Reasoning on Tablescited by 21
  15. 2024Decoding on Graphs: Faithful and Sound Reasoning on Knowledge Graphs through Generation of Well-Formed Chainscited by 20
  16. 2024Interpretable LLM-based Table Question Answeringcited by 18
  17. 2024ALTER: Augmentation for Large-Table-Based Reasoningcited by 9

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 2; 2026: 1.

3 papers.

  1. 2025A Survey of Context Engineering for Large Language Modelscited by 126
  2. 2025Matching Game Preferences Through Dialogical Large Language Models: A Perspectivecited by 0
  3. 2026One Token per Multimodal Evidence: Latent Memory for Resource-Constrained QAcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2025Advancing Reasoning in Large Language Models: Promising Methods and Approaches
  2. 2024Thinking with Knowledge Graphs: Enhancing LLM Reasoning Through Structured Data
  3. 2026KDCM: Reducing Hallucination in LLMs through Explicit Reasoning Structures
  4. 2023A Principled Framework for Knowledge-enhanced Large Language Model
  5. 2025Improving the Reliability of LLMs: Combining CoT, RAG, Self-Consistency, and Self-Verification
  6. 2025LLM Post-Training: A Deep Dive into Reasoning Large Language Models
  7. 2026Double-Calibration: Towards Reliable LLMs via Calibrating Knowledge and Reasoning Confidence
  8. 2024Do Large Language Models Understand Logic or Just Mimick Context?