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 seeds on result stability. Moreover, these metrics conceal potential instabilities in individual predictions and in the organization of embedding spaces. In this work, we conduct a systematic stability analysis of multiple KGEMs across several datasets. We find that high-performance models actually produce divergent predi
The proliferation of AI models, especially in knowledge graph applications, necessitates rigorous evaluation methods that go beyond surface-level metrics to ensure reliability and robustness.
This research highlights critical instabilities in AI model evaluation, particularly for knowledge graph embeddings, which can lead to unreliable AI systems if not addressed.
The focus shifts towards understanding and mitigating the instability introduced by random seeds and the inherent unpredictability of individual predictions in high-performance AI models.
- · AI researchers focused on model robustness
- · Developers of meta-learning and ensemble methods
- · Auditors and evaluators of AI systems
- · Developers solely relying on traditional rank-based metrics
- · Applications built on unstable knowledge graph embeddings
Increased scrutiny on the evaluation protocols and stability of machine learning models.
Development of new metrics and methodologies to assess and report model stability alongside performance.
A potential slowdown in the deployment of certain AI applications until stability concerns are adequately addressed.
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Read at arXiv cs.LG