Task-Aware Evaluation and Error-Overlap Analysis for Large Language Models

AbstractPublic leaderboards for large language models often rely on aggregate scores that conceal critical information about model behavior. In this paper, we present a methodology for task-aware evaluation that combines (i) correctness metrics aligned with task semantics compliance checks for instruction-following and numeric equivalence for mathematics with (ii) pairwise error-overlap analysis to identify complementary model pairs. We apply this methodology to 17 outputs of recent state of the art and frontier LLMs across multiple-choice QA, instruction-following, and mathematical reasoning tasks. We observe that task-aware metrics can reorder model rankings relative to generic lexical metrics, and that error-overlap patterns vary substantially across model pairs and scenarios. We finally conclude by discussing implications for model selection, routing strategies, and LLM-as-judge calibration, and release our analysis pipeline to support further investigation.