Language models (LMs) can perform complex reasoning either end-to-end, with
hidden latent state, or compositionally, with transparent intermediate state.
Composition offers benefits for interpretability and safety, but may need
workflow support and infrastructure to remain competitive. We describe iterated
decomposition, a human-in-the-loop workflow for developing and refining
compositional LM programs. We improve the performance of compositions by
zooming in on failing components and refining them through decomposition,
additional context, chain of thought, etc. To support this workflow, we develop
ICE, an open-source tool for visualizing the execution traces of LM programs.
We apply iterated decomposition to three real-world tasks and improve the
accuracy of LM programs over less compositional baselines: describing the
placebo used in a randomized controlled trial (25% to 65%), evaluating
participant adherence to a medical intervention (53% to 70%), and answering NLP
questions on the Qasper dataset (38% to 69%). These applications serve as case
studies for a workflow that, if automated, could keep ML systems interpretable
and safe even as they scale to increasingly complex tasks.

通过使用透明的中间状态进行复合会带来可解释性和安全性的好处，但可能需要工作流程支持和基础设施来保持竞争力，为此我们描述了一种人在循环中使用的对于开发和完善复合 LM 程序的工作流程，并开发了一个可视化 LM 程序执行跟踪的开源工具 - ICE，通过三个真实世界的案例将这一工作流程应用于 LM 程序，并比非组合基线提高了 LM 程序的准确性 - 描述随机对照试验中使用的安慰剂（25％到 65％），评估参与者遵守医疗干预（53％到 70％）以及在 Qasper 数据集上回答 NLP 问题（38％到 69％）。