Self-Supervised Quantized Representation for KG-LLM Integration

Paper: https://arxiv.org/pdf/2501.18119

This paper proposes a method called Self-Supervised Quantized Representation (SSQR) for seamlessly integrating Knowledge Graphs (KGs) with Large Language Models (LLMs). The key idea is to compress the structural and semantic information of entities in KGs into discrete codes (like tokens in natural language) that can be directly input into LLMs.

Here’s a breakdown:

Problem:

• LLMs are powerful but can suffer from “knowledge hallucination” (making up facts).
• KGs store factual knowledge but are in a graph format, different from the text that LLMs understand.
• Simply converting KG information to text (prompts) for LLMs uses too many tokens and can be inefficient.
• Existing methods for integrating kgs with LLMs either uses sampling, that loses holistic KG information, or introduces extra learnable components, that is hard to be optimized.

Proposed Solution (SSQR):

1. Quantized Representation Learning:
   • Uses a Graph Convolutional Network (GCN) to capture KG structure.
   • Uses Vector Quantization to compress both structural (from the GCN) and semantic (from text descriptions) information into short sequences of discrete codes.
   • Learns these codes in a self-supervised manner, meaning it doesn’t need manual labeling. It reconstructs the KG structure and aligns with semantic text embeddings from a pre-trained LLM.
2. Seamless Integration with LLMs:
   • The learned codes are treated as new “words” (tokens) in the LLM’s vocabulary.
   • KG information can be fed directly to the LLM by simply providing the codes for the relevant entities. No complex prompting or extra networks are needed.
   • The LLM is fine-tuned with instruction data that includes these codes.

Key Contributions:

• First self-supervised method for KG quantization: Learns codes that capture both structure and semantics.
• Seamless integration: The discrete codes allow KGs to be used directly as input to LLMs, expanding the vocabulary instead of requiring complex adaptations.
• Improved performance: Outperforms existing methods on KG link prediction and triple classification tasks, using far fewer tokens than traditional prompting methods. Demonstrates that fine-tuned LLMs (LLaMA2, LLaMA3) perform better with this method.

In simpler terms:

Imagine you have a map (the KG) and a very smart but sometimes forgetful person (the LLM). Instead of describing every detail of the map in words (which is long and tedious), SSQR creates a set of unique, short symbols for each location on the map. You teach the person what these symbols mean, and then you can just give them a few symbols to tell them about a specific place, making communication much faster and more accurate.

Experiments and Results:

• Evaluated on standard KG datasets (WN18RR, FB15k-237, FB15k-237N).
• Shows significant improvements over unsupervised quantization methods and LLM-based methods on KG tasks.
• Analysis shows the learned codes are distinguishable and capture relevant information.
• The fine-tuned LLMs can effectively leverage the quantized representations.