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New framework ReaLM fuses KG embeddings with LLMs; study finds KG embedding models unstable

By PulseAugur Editorial · [2 sources] ·

Researchers have developed ReaLM, a new framework that bridges the gap between knowledge graph embeddings and large language models by discretizing KG embeddings into learnable tokens. This approach allows for a more effective fusion of symbolic and contextual knowledge, outperforming existing methods on benchmark datasets. Separately, a study analyzing knowledge graph embedding models found that high-performing models can produce highly variable predictions and embedding spaces, with random seeds and other stochastic factors significantly impacting results. This instability raises concerns about the reliability of current benchmarking protocols for knowledge graph completion. AI

IMPACT Highlights potential for improved knowledge integration in LLMs, while also raising concerns about the reliability of current KG embedding models.

RANK_REASON Two arXiv papers discussing novel methods and stability issues in knowledge graph embeddings.

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

New framework ReaLM fuses KG embeddings with LLMs; study finds KG embedding models unstable

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Two arXiv papers discussing novel methods and stability issues in knowledge graph embeddings.
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COVERAGE [2]

  1. arXiv cs.AI TIER_1 English(EN) · Wenbin Guo, Xin Wang, Jiaoyan Chen, Lingbing Guo, Zhao Li, Zirui Chen ·

    ReaLM: Residual Quantization Bridging Knowledge Graph Embeddings and Large Language Models

    arXiv:2510.09711v2 Announce Type: replace-cross 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. Howev…

  2. arXiv cs.LG TIER_1 English(EN) · Guillaume M\'erou\'e, Fabien Gandon, Pierre Monnin ·

    Link Prediction or Perdition: the Seeds of Instability in Knowledge Graph Embeddings

    arXiv:2606.03365v1 Announce Type: new Abstract: Embedding models (KGEMs) constitute the main link prediction approach to complete knowledge graphs. Standard evaluation protocols emphasize rank-based metrics such as MRR or Hits@$K$, but usually overlook the influence of random see…

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