Glam: Fine-tuning large language models for domain knowledge graph alignment via neighborhood partitioning and generative subgraph encoding
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Integrating large language models with knowledge graphs derived from domain-specific data represents an important advancement towards more powerful and factual reasoning. As these models grow more capable, it is crucial to enable them to perform multi-step inferences over real-world knowledge graphs while minimizing hallucination. While large language models excel at conversation and text generation, their ability to reason over domain-specialized graphs of interconnected entities remains limited. For example, can we query a model to identify the optimal contact in a professional network for a specific goal, based on relationships and attributes in a private database? The answer is no – such capabilities lie beyond current methods. However, this question underscores a critical technical gap that must be addressed. Many high-value applications in areas such as science, security, and e-commerce rely on proprietary knowledge graphs encoding unique structures, relationships, and logical constraints. We introduce a fine-tuning framework for developing Graph-aligned Language Models (GaLM) that transforms a knowledge graph into an alternate text representation with labeled question-answer pairs. We demonstrate that grounding the models in specific graph-based knowledge expands the models’ capacity for structure-based reasoning. Our methodology leverages the large-language model's generative capabilities to create the dataset and proposes an efficient alternate to retrieval-augmented generation styled methods.
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Knowledge graphs enhance reasoning capabilities by providing structured entity relationships that enable complex multi-hop reasoning and logical inferences, with their rich repository of hierarchical knowledge significantly improving precision and reliability of inferences.
“Knowledge graphs enhance reasoning capabilities by providing structured entity relationships that enable complex multi-hop reasoning and logical inferences, with their rich repository of hierarchical knowledge significantly improving precision and reliability of inferences [1175, 212, 1026].”
From A Survey of Context Engineering for Large Language Models (2025) · cites this paper as [212]
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A Survey of Context Engineering for Large Language Models
Mei, L., Yao, J., Ge, Y., Wang, Y., Bi, B., Cai, Y., Liu, J., Li, M., Li, Z.-Z., Zhang, D., et al. (2025) · arXiv · doi:10.48550/arxiv.2507.13334 · Cited by 13 · see citing works
- Authors
- Dernbach, S., Agarwal, K., Zuniga, A., Henry, M., & Choudhury, S.
- Affiliations
- Pacific Northwest National Laboratory (US)
- Venue
- Proceedings of the AAAI Symposium Series
- Published
- 2024-05-20
- Volume
- 3
- Issue
- 1
- Pages
- 82-89
- ISSN
- 2994-4317 (ISSN-L)
- OpenAlex
- W4398184761
- Cited by
- 14
- References
- 5
- Citations by year
- 2026: 4 · 2025: 9 · 2024: 1
- FWCI
- 4.13
- Type
- conference paper
- Language
- en
- Open access
- diamond
- doi.org
- Locations
- Proceedings of the AAAI Symposium Series (publishedVersion)
- arXiv (Cornell University) (submittedVersion, cc-by)
- arXiv (Cornell University) (cc-by)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Physical Sciences › Computer Science › Artificial Intelligence › Advanced Graph Neural Networks
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Multimodal Machine Learning Applications
- Keywords
- large language models, knowledge graphs, graph alignment, fine-tuning, graph reasoning, hallucination mitigation, domain-specific knowledge graphs, multi-step reasoning
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