Routoo: Learning To Route To Large Language Models Effectively
Mohammadshahi Alireza, Shaikh Arshad Rafiq, Yazdani Majid. Arxiv 2024
[Paper]
Developing foundational large language models (LLMs) is becoming increasingly costly and inefficient. Also, closed-source and larger open-source models generally offer better response quality but come with higher inference costs than smaller models. In this paper, we introduce Routoo, an architecture designed to optimize the selection of LLMs for specific prompts based on performance, cost, and efficiency. Routoo consists of two key components: a performance predictor and a cost-aware decoding. The performance predictor is a lightweight LLM that estimates the performance of various underlying LLMs without needing to execute and evaluate them. The cost-aware decoding then selects the most suitable model based on these predictions and other constraints like cost and latency. We evaluated Routoo using the MMLU benchmark across 57 domains employing open-source models. Our results show that Routoo matches the performance of the Mixtral 8x7b model while reducing inference costs by one-third. Additionally, by allowing increased costs, Routoo surpasses Mixtral’s accuracy by over 5% at equivalent costs, achieving an accuracy of 75.9%. When integrating GPT4 into our model pool, Routoo nearly matches GPT4’s performance at half the cost and exceeds it with a 25% cost reduction. These outcomes highlight Routoo’s potential to create new SOTA in a cost-effective manner by leveraging the collective knowledge of multiple LLMs.
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