Papers with KG only models

1 papers
Model-Agnostic Bias Measurement in Link Prediction (2023.findings-eacl)

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Challenge: Existing work investigating social bias in factual knowledge graphs has focused on knowledge graph embeddings, so more recent classes of models achieving superior results by fine-tuning Transformers have not yet been investigated.
Approach: They propose a model-agnostic approach for bias measurement leveraging fairness metrics to compare bias in knowledge graph embedding-based predictions (KG only) with models that use pre-trained, Transformer-based language models (KG+LM).
Outcome: The proposed model-agnostic approach compares gender bias in occupation predictions with models that use pre-trained, Transformer-based language models (KG+LM).

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