A new benchmarking study finds moderate but domain-dependent divergence in how LLMs retrieve and rank APIs, with higher disagreement on open-ended tasks.
Further generalizations of the Jaccard index
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Heterogeneous graph neural networks with post-hoc explanations improve accuracy on six land-use indicators from mobility data and provide feature attribution and counterfactual insights aligned with commuting patterns.
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Quantifying Divergence in Inter-LLM Communication Through API Retrieval and Ranking
A new benchmarking study finds moderate but domain-dependent divergence in how LLMs retrieve and rank APIs, with higher disagreement on open-ended tasks.
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Heterogeneous Graph Neural Networks with Post-hoc Explanations for Multi-modal and Explainable Land Use Inference
Heterogeneous graph neural networks with post-hoc explanations improve accuracy on six land-use indicators from mobility data and provide feature attribution and counterfactual insights aligned with commuting patterns.