Researchers and practitioners operating on a limited budget face the
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 sequentially, or a routing strategy is used, where only one model is
ever called. Both scenarios are dependent on a decision criterion which is
typically implemented by an extra neural model. In this work, we propose a
simpler 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.

通过使用仅基于小型 LLM 生成的不确定性作为决策准则，我们提出了一种简化的解决方案，该解决方案在 25 个实验设置中优于现有方法，实现了成本和性能的最佳平衡。