GRALAN: When Knowledge Graphs Speak LLM Native

Large Language Models excel at reasoning but lack grounding. Knowledge Graphs provide grounding but speak a different language. Integrating these paradigms has been challenging — until now.

GRALAN (Graph Language) enables KGs to speak directly in the LLM's semantic space through relational tokens that preserve graph structure. A trainable language mediator generates structured tokens for any frozen LLM, creating a foundation for knowledge-intensive applications.

The result: significant improvement on complex multi-hop reasoning tasks without fine-tuning the base model.

The KG-LLM Integration Problem

Knowledge Graphs and LLMs have complementary strengths:

LLMs	Knowledge Graphs
Fluent generation	Structured facts
Reasoning ability	Precise relationships
Broad knowledge	Grounded evidence
But: Hallucinate	But: Rigid querying

The challenge: how to combine them without losing either's strengths?

Existing approaches fall into three categories:

1. Embedding-Based Integration

Encode KG entities as vectors, feed to LLM.

Problem: Loses structural information. Relationships become distances in vector space.

2. Prompt-Based Integration

Describe KG subgraphs in natural language prompts.

Problem: Token-heavy. Structure is flattened to text. Context window limits apply.

3. Fine-Tuning Integration

Fine-tune LLM on KG-augmented data.

Problem: Expensive. Loses base model capabilities. Not portable across models.

GRALAN takes a different approach: language mediation.

GRALAN's Architecture

1. Relational Token Vocabulary

GRALAN introduces a new token type: relational tokens that encode graph structure directly in the token space.

Standard tokens:     [Paris] [is] [capital] [of] [France]
Relational tokens:   [Paris] [<entity>] [capital_of] [France] [<entity>]

The relational tokens <entity> and capital_of are learned embeddings that preserve:

• Entity type: Person, Location, Organization
• Relationship type: capital_of, located_in, founded_by
• Direction: Subject → Object
• Confidence: Edge weight from KG

2. Trainable Language Mediator

The mediator is a lightweight transformer that sits between the KG and the frozen LLM: