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.

翻译：符号（或更广泛地，非自然语言的文本表征）如数值序列、分子式和表格分隔符广泛存在，在抽象推理、化学性质预测和表格问答等任务中发挥重要作用。尽管大语言模型（LLMs）具有令人瞩目的自然语言理解能力，但其符号推理能力仍显不足，这归因于符号表征与通用自然语言之间的差异。我们提出符号到语言（S2L）方法——一种无需微调的方法，使大语言模型能够通过自然语言表达的信息解决符号相关问题。具体而言，S2L首先将涉及符号转换为基于语言的表征（可通过提示LLMs或利用外部工具实现），随后通过直接替换或拼接将这些基于语言的表征整合到原始问题中，作为LLMs的有效输入信息。我们使用基于API（GPT-4、ChatGPT）和开源（OpenChat）模型在八项符号相关任务（涵盖纯符号抽象推理到社交媒体情感分析）上评估S2L方法。实验结果表明，S2L持续带来性能提升。例如，在GPT-4上应用S2L后，1D-ARC和Dyck语言子任务的平均显著提升分别达到+21.9%和+9.5%。代码与数据见https://github.com/THUNLP-MT/symbol2language。