In this paper, we take the initiative to investigate the performance of LLMs on complex planning tasks that require LLMs to understand a virtual spatial environment simulated via natural language and act correspondingly in text. We propose a benchmark named Natural Language Planning (NLP) composed of a set of novel tasks: Brick World, NLVR-based Manipulations, and Natural Language Navigation. We found that current popular LLMs such as ChatGPT still lack abilities in complex planning. This arises a question -- do the LLMs have a good understanding of the environments described in natural language, or maybe other alternatives such as symbolic representations are neater and hence better to be understood by LLMs? To this end, we propose a novel method called CoS (Chain-of-Symbol Prompting) that represents the complex environments with condensed symbolic spatial representations during the chained intermediate thinking steps. CoS is easy to use and does not need additional training on LLMs. Extensive experiments indicate that CoS clearly surpasses the performance of the Chain-of-Thought (CoT) Prompting in all three planning tasks with even fewer tokens used in the inputs compared with CoT on ChatGPT and InstructGPT. The performance gain is strong, by up to 60.8% accuracy (from 31.8% to 92.6%) on Brick World for ChatGPT. CoS also reduces the number of tokens in the prompt obviously, by up to 65.8% of the tokens (from 407 to 139) for the intermediate steps from demonstrations on Brick World.

翻译：本文率先探究大型语言模型在复杂规划任务中的表现，此类任务要求LLMs理解通过自然语言模拟的虚拟空间环境并做出相应文本操作。我们提出名为自然语言规划（NLP）的基准测试集，包含三项新型任务：砖块世界、基于NLVR的操作及自然语言导航。研究发现，当前主流LLMs（如ChatGPT）在复杂规划方面仍能力不足。这引发一个疑问——LLMs是否真正理解自然语言描述的环境，抑或符号表示等替代方式更简洁、更易被LLMs理解？为此，我们提出CoS（链式符号提示）方法，在链式中介推理步骤中用紧凑的符号空间表示复杂环境。CoS易于使用且无需对LLMs进行额外训练。大量实验表明，在ChatGPT和InstructGPT上，CoS在所有三项规划任务中均显著超越思维链（CoT）提示的性能，且输入令牌使用量更少。以ChatGPT在砖块世界任务为例，性能提升高达60.8%（准确率从31.8%提升至92.6%）。CoS还显著减少提示中的令牌数量，在砖块世界演示的中介步骤中，令牌数最多减少65.8%（从407个降至139个）。