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The DivAlgo benchmark is proposed, a large-scale dataset constituting algorithmically diverse solutions to programming problems that is significantly less diverse than human solutions and introduces a lightweight similarity metric to enable large-scale evaluation.
Human-centered artificial intelligence models that identify suspected overdose deaths using narrative reports from medicolegal death investigations and describe ethical considerations for using AI in overdose surveillance are developed and evaluated.
This work critically examines the effectiveness of the most common metrics used in the field, such as BLEU, embeddings, and LLMs-as-judges, and finds strong evidence that employing ensembles of diverse evaluation metrics consistently outperforms single-evaluator methods.
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