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。

基于不确定性的两层选择对大型语言模型的优化调用

Optimising Calls to Large Language Models with Uncertainty-Based  Two-Tier Selection

The high cost of full-parameter fine-tuning (FFT) of Large Language Models
(LLMs) has led to a series of parameter-efficient fine-tuning (PEFT) methods.
However, it remains unclear which methods provide the best cost-performance
trade-off at different model scales. We introduce Astraios, a suite of 28
instruction-tuned OctoCoder models using 7 tuning methods and 4 model sizes up
to 16 billion parameters. Through investigations across 5 tasks and 8 different
datasets encompassing both code comprehension and code generation tasks, we
find that FFT generally leads to the best downstream performance across all
scales, and PEFT methods differ significantly in their efficacy based on the
model scale. LoRA usually offers the most favorable trade-off between cost and
performance. Further investigation into the effects of these methods on both
model robustness and code security reveals that larger models tend to
demonstrate reduced robustness and less security. At last, we explore the
relationships among updated parameters, cross-entropy loss, and task
performance. We find that the tuning effectiveness observed in small models
generalizes well to larger models, and the validation loss in instruction
tuning can be a reliable indicator of overall downstream performance.

基于 28 个调优方法和 4 个模型尺寸，通过对 5 项任务和 8 个不同数据集的研究，我们发现完全参数微调通常在所有尺度上都能提供最佳的下游性能，并且参数高效微调方法在模型规模上的功效存在显著差异。然而，LoRA 方法通常在成本和性能之间提供了最有利的平衡。此外，我们还发现较大的模型往往表现出较低的稳健性和较低的安全性。最后，我们探讨了更新的参数、交叉熵损失和任务性能之间的关系，并发现在小型模型中观察到的调优效果在大型模型中也普遍适用，指导性调优中的验证损失可以作为总体下游性能的可靠指标。