Integrating large language models (LLMs) 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 LLM 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 (GLaM) 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.

我们介绍了一种用于开发图形对齐的语言模型（GLaM）的微调框架，该框架将知识图形转化为具有标签的问题 - 答案对的替代文本表示。我们证明，以特定基于图形的知识为基础的模型具有更强的结构化推理能力。我们的方法利用大型语言模型的生成能力创建数据集，并提出了一种效率高于检索增强型生成方法的替代方法。