MO-CAPO introduces a budget-aware multi-objective optimizer that jointly tunes LLM prompt performance and inference cost, producing diverse Pareto fronts more efficiently than standard NSGA-II.
InFindings of the Association for Computational Linguistics: ACL 2025, Wanxiang Che, Joyce Nabende, Ekaterina Shutova, and Mohammad Taher Pilehvar (Eds.)
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MO-CAPO: Multi-Objective Cost-Aware Prompt Optimization
MO-CAPO introduces a budget-aware multi-objective optimizer that jointly tunes LLM prompt performance and inference cost, producing diverse Pareto fronts more efficiently than standard NSGA-II.