GS-Quant: Granular Semantic and Generative Structural Quantization for Knowledge Graph Completion
📰 ArXiv cs.AI
Learn how GS-Quant addresses the modality gap in Knowledge Graph Completion using granular semantic and generative structural quantization, enabling more effective LLM-based KGC.
Action Steps
- Apply GS-Quant to align continuous graph embeddings and discrete LLM tokens
- Use granular semantic quantization to preserve hierarchical relationships in knowledge graphs
- Implement generative structural quantization to generate semantically disentangled codes
- Evaluate the performance of GS-Quant on KGC benchmarks
- Compare GS-Quant with existing quantization-based approaches for KGC
Who Needs to Know This
Researchers and engineers working on Knowledge Graph Completion and Large Language Models can benefit from this article to improve their understanding of quantization techniques and modalities alignment.
Key Insight
💡 GS-Quant addresses the modality gap by using granular semantic and generative structural quantization to align continuous graph embeddings and discrete LLM tokens.
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🚀 GS-Quant: Bridging the modality gap in Knowledge Graph Completion with granular semantic and generative structural quantization! 🤖
Full Article
Title: GS-Quant: Granular Semantic and Generative Structural Quantization for Knowledge Graph Completion
Abstract:
arXiv:2604.21649v1 Announce Type: new Abstract: Large Language Models (LLMs) have shown immense potential in Knowledge Graph Completion (KGC), yet bridging the modality gap between continuous graph embeddings and discrete LLM tokens remains a critical challenge. While recent quantization-based approaches attempt to align these modalities, they typically treat quantization as flat numerical compression, resulting in semantically entangled codes that fail to mirror the hierarchical nature of human
Abstract:
arXiv:2604.21649v1 Announce Type: new Abstract: Large Language Models (LLMs) have shown immense potential in Knowledge Graph Completion (KGC), yet bridging the modality gap between continuous graph embeddings and discrete LLM tokens remains a critical challenge. While recent quantization-based approaches attempt to align these modalities, they typically treat quantization as flat numerical compression, resulting in semantically entangled codes that fail to mirror the hierarchical nature of human
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