Optimising calls to large language models with uncertainty-based two-tier selection

Researchers and practitioners operating on a limited budget face the well-known cost-performance trade-off dilemma. The challenging decision often centers on whether to use a large LLM with better performance or a smaller one with reduced costs. This has motivated recent research in the optimisation of LLM calls. Either a cascading strategy is used, where a smaller LLM or both are called causally, or a routing strategy is used, where only one model is ever called. This is dependent on a decision criterion in both scenarios which is typically an auxiliary neural model. In this work, we propose a cost-effective solution; we use only the uncertainty of the generations of the small LLM as the decision criterion. We compare our approach with both cascading and routing strategies using three different pairs of pre-trained small and large LLMs, on nine different tasks and against approaches that require an additional neural model. Our experiments reveal this simple solution optimally balances cost and performance, outperforming existing methods on 25 out of 27 experimental setups.