GLaM: Fine-Tuning Large Language Models for Domain Knowledge Graph Alignment via Neighborhood Partitioning and Generative Subgraph Encoding
CoRR(2024)
摘要
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.
更多查看译文
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要