Large language model (LLM) performance on reasoning problems typically does
not generalize out of distribution. Previous work has claimed that this can be
mitigated by modifying prompts to include examples with chains of
thought--demonstrations of solution procedures--with the intuition that it is
possible to in-context teach an LLM an algorithm for solving the problem. This
paper presents a case study of chain of thought on problems from Blocksworld, a
classical planning domain, and examine the performance of two state-of-the-art
LLMs across two axes: generality of examples given in prompt, and complexity of
problems queried with each prompt. While our problems are very simple, we only
find meaningful performance improvements from chain of thought prompts when
those prompts are exceedingly specific to their problem class, and that those
improvements quickly deteriorate as the size n of the query-specified stack
grows past the size of stacks shown in the examples. Our results hint that,
contrary to previous claims in the literature, CoT's performance improvements
do not stem from the model learning general algorithmic procedures via
demonstrations and depend on carefully engineering highly problem specific
prompts. This spotlights drawbacks of chain of thought, especially because of
the sharp tradeoff between possible performance gains and the amount of human
labor necessary to generate examples with correct reasoning traces.

根据 Blocksworld 的案例研究，通过链式思维问题可以提高大型语言模型的性能，但需要高度问题特定的提示，并且存在性能改善和生成正确推理示例所需人力之间的明显权衡。