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Learning to Route LLMs from Implicit Cost-Performance Preferences via Meta-Learning
Large language models (LLMs) present a trade-off between performance and cost, where more powerful models incur greater expense. LLM routing aims to mitigate expenses while maintaining performance by sending queries to the most suitable model. However, existing methods cannot perform well for differ
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