arXiv:2510.09711v2 Announce Type: replace Abstract: Large Language Models (LLMs) have recently emerged as a powerful paradigm for Knowledge Graph Completion (KGC), offering strong reasoning and generalization capabilities beyond traditional embedding-based approaches. However, existing LLM-based methods often struggle to fully exploit structured semantic representations, as the continuous embedding space of pretrained KG models is fundamentally misaligned with the discrete token space of LLMs. This discrepancy hinders effective semantic transfer and limits their performance. To address this ch
The rapid advancement of Large Language Models has made their integration with existing knowledge representation systems a critical research frontier, addressing their current limitations.
This development, if successful, could significantly enhance the reasoning capabilities and knowledge utilization of LLMs, making them more powerful and reliable for complex tasks.
The method proposes a novel approach to bridge the semantic gap between continuous Knowledge Graph embeddings and discrete LLM token spaces, potentially leading to more effective knowledge transfer.
- · LLM Developers
- · AI Researchers
- · Knowledge Graph Providers
- · Traditional symbolic AI approaches
Improved performance of LLMs in knowledge-intensive tasks like question answering and factual consistency.
Accelerated development of more robust and accurate AI systems that leverage structured and unstructured data.
New AI applications emerging from the enhanced ability of LLMs to reason over vast knowledge bases.
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