Communicating Chess Strategies in Natural Language

Langyuan Cui; Chun Kai Ling; Hwee Tou Ng

Communicating Chess Strategies in Natural Language

Langyuan Cui, Chun Kai Ling, Hwee Tou Ng

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Abstract

Chess engines have long achieved superhuman playing strength. However, the underlying strategy behind their move suggestions is difficult for human players, even skilled ones, to comprehend. Motivated by this, we propose the task of chess *strategy verbalization*, which is to describe chess strategies in natural language. We design (i) a pipeline for verbalizing strategies and (ii) an evaluation framework for objective evaluation of generated strategy descriptions. Our experiments show that natural language is a promising and interpretable medium for communicating strategic information to both human and LLM players. We glean additional interesting insights, including (a) the importance of evaluating strategies beyond the main line, (b) the limitations of pure concept-based descriptions, and (c) the limitations of relying on LLMs rather than humans for evaluation.

Anthology ID:: 2026.findings-emnlp.1560
Volume:: Findings of the Association for Computational Linguistics: EMNLP 2026
Month:: October
Year:: 2026
Address:: Budapest, Hungary
Editors:: Hung-Yi Lee, Sunipa Dev, Jessy Li, Desmond Elliott
Venue:: Findings
SIG:: SIGDAT
Publisher:: Association for Computational Linguistics
Note:
Pages:: 33231–33252
Language:
URL:: https://aclanthology.org/2026.findings-emnlp.1560/
DOI:
Bibkey:
Cite (ACL):: Langyuan Cui, Chun Kai Ling, and Hwee Tou Ng. 2026. Communicating Chess Strategies in Natural Language. In Findings of the Association for Computational Linguistics: EMNLP 2026, pages 33231–33252, Budapest, Hungary. Association for Computational Linguistics.
Cite (Informal):: Communicating Chess Strategies in Natural Language (Cui et al., Findings 2026)
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PDF:: https://aclanthology.org/2026.findings-emnlp.1560.pdf

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@inproceedings{cui-etal-2026-communicating,
    title = "Communicating Chess Strategies in Natural Language",
    author = "Cui, Langyuan  and
      Ling, Chun Kai  and
      Ng, Hwee Tou",
    editor = "Lee, Hung-Yi  and
      Dev, Sunipa  and
      Li, Jessy  and
      Elliott, Desmond",
    booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {EMNLP} 2026",
    month = oct,
    year = "2026",
    address = "Budapest, Hungary",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2026.findings-emnlp.1560/",
    pages = "33231--33252",
    ISBN = "979-8-89176-462-0",
    abstract = "Chess engines have long achieved superhuman playing strength. However, the underlying strategy behind their move suggestions is difficult for human players, even skilled ones, to comprehend. Motivated by this, we propose the task of chess *strategy verbalization*, which is to describe chess strategies in natural language. We design (i) a pipeline for verbalizing strategies and (ii) an evaluation framework for objective evaluation of generated strategy descriptions. Our experiments show that natural language is a promising and interpretable medium for communicating strategic information to both human and LLM players. We glean additional interesting insights, including (a) the importance of evaluating strategies beyond the main line, (b) the limitations of pure concept-based descriptions, and (c) the limitations of relying on LLMs rather than humans for evaluation."
}

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%0 Conference Proceedings
%T Communicating Chess Strategies in Natural Language
%A Cui, Langyuan
%A Ling, Chun Kai
%A Ng, Hwee Tou
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Findings of the Association for Computational Linguistics: EMNLP 2026
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-462-0
%F cui-etal-2026-communicating
%X Chess engines have long achieved superhuman playing strength. However, the underlying strategy behind their move suggestions is difficult for human players, even skilled ones, to comprehend. Motivated by this, we propose the task of chess *strategy verbalization*, which is to describe chess strategies in natural language. We design (i) a pipeline for verbalizing strategies and (ii) an evaluation framework for objective evaluation of generated strategy descriptions. Our experiments show that natural language is a promising and interpretable medium for communicating strategic information to both human and LLM players. We glean additional interesting insights, including (a) the importance of evaluating strategies beyond the main line, (b) the limitations of pure concept-based descriptions, and (c) the limitations of relying on LLMs rather than humans for evaluation.
%U https://aclanthology.org/2026.findings-emnlp.1560/
%P 33231-33252

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Markdown (Informal)

[Communicating Chess Strategies in Natural Language](https://aclanthology.org/2026.findings-emnlp.1560/) (Cui et al., Findings 2026)

Communicating Chess Strategies in Natural Language (Cui et al., Findings 2026)

ACL

Langyuan Cui, Chun Kai Ling, and Hwee Tou Ng. 2026. Communicating Chess Strategies in Natural Language. In Findings of the Association for Computational Linguistics: EMNLP 2026, pages 33231–33252, Budapest, Hungary. Association for Computational Linguistics.