  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 and Action (Natala)
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. Code and data available at:
https://github.com/hanxuhu/chain-of-symbol-planning
