  Symbols (or more broadly, non-natural language textual representations) such
as numerical sequences, molecular formulas, and table delimiters widely exist,
playing important roles in various tasks such as abstract reasoning, chemical
property prediction, and table question answering. Despite the impressive
natural language comprehension capabilities of large language models (LLMs),
their reasoning abilities for symbols remain inadequate, which could attributed
to the difference between symbol representations and general natural languages.
We propose symbol-to-language (S2L), a tuning-free method that enables large
language models to solve symbol-related problems with information expressed in
natural language. Specifically, S2L first converts the symbols involved to
language-based representations, which can be implemented by prompting LLMs or
leveraging external tools, then these language-based representations are
integrated into the original problem via direct substitution or concatenation,
serving as useful input information for LLMs. We evaluate the S2L method using
both API-based (GPT-4, ChatGPT) and open-source (OpenChat) models over eight
symbol-related tasks, ranging from symbol-only abstract reasoning to sentiment
analysis in social media. Experimental results show that S2L consistently leads
to superior performance. For example, by employing S2L for GPT-4, there can be
average significant improvements of +21.9% and +9.5% for subtasks in 1D-ARC and
Dyck language, respectively. Codes and data are available at
https://github.com/THUNLP-MT/symbol2language.
