Challenge: Large language models (LLMs) have demonstrated human-level performance on a vast spectrum of natural language tasks.
Approach: They propose a method to infuse structured knowledge into large language models by directly training T5 models on factual triples of knowledge graphs (KGs).
Outcome: The proposed method outperforms baseline models on FreebaseQA and WikiHop, as well as the Wikidata-answerable subset of TriviaQA and NaturalQuestions.

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Efficient Knowledge Infusion via KG-LLM Alignment (2024.findings-acl)

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Challenge: Existing methods for knowledge infusion face knowledge mismatch and poor information compliance of LLMs with knowledge graphs.
Approach: They propose a three-stage alignment strategy to enhance the LLM's capability to utilize information from knowledge graphs.
Outcome: The proposed method outperforms baselines on biomedical question-answering datasets and outperformed existing methods.
Enhancing Multilingual Language Model with Massive Multilingual Knowledge Triples (2022.emnlp-main)

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Challenge: Existing methods for language model pretraining use limited knowledge graph data for knowledge-intensive tasks.
Approach: They propose to make better use of multilingual annotations and language agnostic properties of KG triples for pretraining LMs.
Outcome: The proposed models show significant performance improvements on a wide range of knowledge-intensive cross-lingual tasks.
InfuserKI: Enhancing Large Language Models with Knowledge Graphs via Infuser-Guided Knowledge Integration (2024.findings-emnlp)

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Challenge: Large Language Models have exceptional capabilities in open generation, yet they encounter difficulties with tasks that require intensive knowledge.
Approach: They propose a framework that integrates unknown knowledge into LLMs without overlap . they propose integrating domain-specific knowledge graphs into Llms to reduce knowledge forgetting .
Outcome: The proposed framework outperforms state-of-the-art baselines in integrating new knowledge into LLMs.
Knowledge Graph Based Synthetic Corpus Generation for Knowledge-Enhanced Language Model Pre-training (2021.naacl-main)

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Challenge: Existing work on data-to-text generation focused on domain-specific benchmark datasets.
Approach: They use a KG-Wikipedia text aligned corpus to verbalize the entire English Wikidata KG . they show that this approach can be used to integrate structured KGs and natural language corpora .
Outcome: The proposed method improves on open domain QA and the LAMA knowledge probe.
Filter-then-Generate: Large Language Models with Structure-Text Adapter for Knowledge Graph Completion (2025.coling-main)

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Challenge: Empirical evidence suggests that LLMs perform worse than conventional KGC approaches.
Approach: They propose a filter-then-generate paradigm and a multiple-choice question format to harness the capability of LLMs while mitigating the issue casused by hallucinations.
Outcome: The proposed method achieves substantial performance gain compared to existing state-of-the-art methods.
Knowledge Graph-Enhanced Large Language Models via Path Selection (2024.findings-acl)

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Challenge: Large Language Models (LLMs) have shown unprecedented performance in various real-world applications, but they are known to generate factually inaccurate outputs.
Approach: They propose a framework to integrate external knowledge extracted from Knowledge Graphs (KGs) they propose to generate scores for knowledge paths with input texts via latent semantic matching.
Outcome: Experiments on real-world datasets validate the effectiveness of a framework to extract knowledge from Knowledge Graphs (KGs) incorporating external knowledge has become a promising strategy to improve the factual accuracy of LLM-generated outputs.
Retrieval and Reasoning on KGs: Integrate Knowledge Graphs into Large Language Models for Complex Question Answering (2024.findings-emnlp)

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Challenge: Large Language Models (LLMs) have performed impressively in various NLP tasks, but their inherent hallucination phenomena severely challenge their credibility in complex reasoning.
Approach: They propose to integrate explainable Knowledge Graphs (KGs) with LLMs to alleviate hallucinations . they construct subgraphs to enhance the retrieval capabilities of KGs via CoT reasoning.
Outcome: Extensive experiments on two KGQA datasets show that the proposed model achieves convincing performance compared to strong baselines.
Exploring Graph Learning Tasks with Pure LLMs: A Comprehensive Benchmark and Investigation (2026.findings-acl)

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Challenge: Recent studies focus on performance benchmarks without fully comparing LLMs to graph learning models.
Approach: They evaluate off-the-shelf and instruction-tuned graph learning models across a variety of scenarios.
Outcome: The proposed models outperform traditional graph learning models in few-shot settings, the authors show . their models out perform models with instruction tuning, and they show excellent generalization and robustness.
KG-GPT: A General Framework for Reasoning on Knowledge Graphs Using Large Language Models (2023.findings-emnlp)

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Challenge: Using large language models for complex reasoning tasks on knowledge graphs remains unexplored.
Approach: They propose a multi-purpose framework leveraging large language models for complex reasoning tasks on knowledge graphs.
Outcome: The proposed framework outperforms fully-supervised models in KG-based fact verification and KGQA benchmarks.
Digest the Knowledge: Large Language Models empowered Message Passing for Knowledge Graph Question Answering (2025.acl-long)

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Challenge: Existing methods to augment large language models (LLMs) with external knowledge are unorganized and unorganized.
Approach: They propose a method that learns a concise facts graph and encodes it into multi-level lists of texts to augment LLMs.
Outcome: The proposed method improves on all 5 knowledge graph question answering datasets and offers human-level semantic explainability.

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