Recently, Large Language Models (LLMs) have demonstrated remarkable capabilities. Chain-of-Thought (CoT) has been proposed as a way of assisting LLMs in performing complex reasoning. However, developing effective prompts can be a challenging and labor-intensive task. Many studies come out of some way to automatically construct CoT from test data. Most of them assume that all test data is visible before testing and only select a small subset to generate rationales, which is an unrealistic assumption. In this paper, we present a case study on how to construct and optimize chain-of-thought prompting using batch data in streaming settings.

翻译：近年来，大型语言模型（LLMs）展现了卓越的能力。思维链（Chain-of-Thought，CoT）被提出作为辅助LLMs进行复杂推理的一种方法。然而，开发有效的提示语是一项具有挑战性且劳动密集型的任务。许多研究提出了从测试数据中自动构建CoT的方法，其中大多数假设所有测试数据在测试前可见，并仅选择一小部分子集来生成推理依据，这在实际中是不现实的假设。本文通过一个案例研究，探讨如何在流式场景下利用批处理数据构建和优化思维链提示。