AP-BMM approximates Pareto sets of layer-wise merged LLMs for accuracy-cost trade-offs via prior-guided asynchronous Bayesian optimization and reranking.
It’s morphing time: Unleashing the potential of multiple llms via multi-objective optimization.IEEE Transactions on Evolutionary Computation,
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AP-BMM: Approximating Capability-Cost Pareto Sets of LLMs via Asynchronous Prior-Guided Bayesian Model Merging
AP-BMM approximates Pareto sets of layer-wise merged LLMs for accuracy-cost trade-offs via prior-guided asynchronous Bayesian optimization and reranking.