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Knowledge-Augmented Language Model Prompting for Zero-Shot Knowledge Graph Question Answering

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arxiv 2306.04136 v1 pith:CVLNP6IS submitted 2023-06-07 cs.CL

Jinheon Baek , Alham Fikri Aji , Amir Saffari This is my paper · ORCID
classification cs.CL
keywords knowledgequestionllmsfactsgraphzero-shotansweringinput
verification ladder T0 review T1 audit T2 compute T3 formal
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Large Language Models (LLMs) are capable of performing zero-shot closed-book question answering tasks, based on their internal knowledge stored in parameters during pre-training. However, such internalized knowledge might be insufficient and incorrect, which could lead LLMs to generate factually wrong answers. Furthermore, fine-tuning LLMs to update their knowledge is expensive. To this end, we propose to augment the knowledge directly in the input of LLMs. Specifically, we first retrieve the relevant facts to the input question from the knowledge graph based on semantic similarities between the question and its associated facts. After that, we prepend the retrieved facts to the input question in the form of the prompt, which is then forwarded to LLMs to generate the answer. Our framework, Knowledge-Augmented language model PromptING (KAPING), requires no model training, thus completely zero-shot. We validate the performance of our KAPING framework on the knowledge graph question answering task, that aims to answer the user's question based on facts over a knowledge graph, on which ours outperforms relevant zero-shot baselines by up to 48% in average, across multiple LLMs of various sizes.

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Cited by 5 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. GARDRec: Decision-Level Graph Grounding for Large Language Model Recommendation

    cs.IR 2026-08 conditional novelty 6.0 of 10

    GARDRec improves LLM-based next-item ranking by grounding decisions in knowledge-graph embeddings, personalized graph contexts, and late-stage scoring rather than prompt text.

  2. Improving Factuality for Dialogue Response Generation via Graph-Based Knowledge Augmentation

    cs.CL 2025-06 conditional novelty 5.0 of 10

    The paper proposes TG-DRG and GA-DRG, two graph-augmented frameworks that combine coreference resolution, knowledge selection, and graph encoding to improve factuality of dialogue responses, evaluated with a newly pro...

  3. Fusing Knowledge and Language: A Comparative Study of Knowledge Graph-Based Question Answering with LLMs

    cs.AI 2025-09 reject novelty 4.0 of 10

    In a small comparative study, GraphRAG outscored spaCy and CoreNLP-based KG-QA pipelines on reasoning-heavy questions, but the evaluation design conflates method choice with pipeline architecture.

  4. Explainable Knowledge Graph Retrieval-Augmented Generation (KG-RAG) with KG-SMILE

    cs.AI 2025-09 reject novelty 4.0 of 10

    KG-SMILE applies perturbation and linear regression to a knowledge graph to attribute which entities and relations drive a GraphRAG system's answers.

  5. Improving TCM Question Answering through Tree-Organized Self-Reflective Retrieval with LLMs

    cs.CL 2025-02 conditional novelty 4.0 of 10

    A tree-organized, self-reflective retrieval framework over a TCM knowledge base lifts GPT-4 accuracy on a 600-question licensing-exam sample by 19.85 absolute percentage points.

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