Paper Link : https://arxiv.org/pdf/2406.18665v2
Paper Title: RouteLLM: Learning to Route LLMs with Preference Data
With the growing capabilities of large language models (LLMs), efficiently utilizing them becomes crucial. LLM routing emerges as a promising solution. It directs user queries to the most suitable LLM based on factors like complexity and domain. This approach aims to optimize response quality while minimizing costs. However, optimal routing presents a challenge: the router model needs to understand the query’s intent, complexity, and domain, along with the capabilities of candidate LLMs. Additionally, it should be economical, fast, and adaptable to new, improved models.
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