FlexRouter: Learning Complementary Model Sets for Flexible LLM Routing

FlexRouter is a routing framework for large language models that explicitly models model complementarity to maximize answer coverage. It formulates routing as a coverage-oriented subset selection problem and uses Determinantal Point Processes to capture both competence and redundancy. During inference, a greedy strategy based on marginal log-determinant gains allows the router to adaptively determine subset sizes without a fixed budget, achieving higher coverage with lower redundancy on the RouterEval benchmark.

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• llms
• benchmarks