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    <abstract>This paper introduces LingGym, a new benchmark that evaluates LLMs’ capacity for meta-linguistic reasoning using Interlinear Glossed Text (IGT) and grammatical descriptions extracted from 18 typologically diverse reference grammars. Unlike previous work that focuses on specific downstream tasks, we assess whether LLMs can generalize linguistic inference across low-resource languages and structures not seen during training. We present a controlled evaluation task: Word-Gloss Inference, in which the model must infer a missing word and gloss from context using varying levels of linguistic information (e.g., glosses, grammatical explanations, translations). Our results show that incorporating structured linguistic cues leads to consistent improvements in reasoning performance across all models. This work highlights both the promise and current limitations of using LLMs for typologically informed linguistic analysis and low-resource language documentation.</abstract>
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%0 Conference Proceedings
%T LingGym: How Far Are LLMs from Thinking Like Field Linguists?
%A Yang, Changbing
%A Ma, Franklin
%A Shi, Freda
%A Zhu, Jian
%Y Christodoulopoulos, Christos
%Y Chakraborty, Tanmoy
%Y Rose, Carolyn
%Y Peng, Violet
%S Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou, China
%@ 979-8-89176-332-6
%F yang-etal-2025-linggym
%X This paper introduces LingGym, a new benchmark that evaluates LLMs’ capacity for meta-linguistic reasoning using Interlinear Glossed Text (IGT) and grammatical descriptions extracted from 18 typologically diverse reference grammars. Unlike previous work that focuses on specific downstream tasks, we assess whether LLMs can generalize linguistic inference across low-resource languages and structures not seen during training. We present a controlled evaluation task: Word-Gloss Inference, in which the model must infer a missing word and gloss from context using varying levels of linguistic information (e.g., glosses, grammatical explanations, translations). Our results show that incorporating structured linguistic cues leads to consistent improvements in reasoning performance across all models. This work highlights both the promise and current limitations of using LLMs for typologically informed linguistic analysis and low-resource language documentation.
%U https://aclanthology.org/2025.emnlp-main.69/
%P 1314-1340

Markdown (Informal)

[LingGym: How Far Are LLMs from Thinking Like Field Linguists?](https://aclanthology.org/2025.emnlp-main.69/) (Yang et al., EMNLP 2025)

• LingGym: How Far Are LLMs from Thinking Like Field Linguists? (Yang et al., EMNLP 2025)

ACL

• Changbing Yang, Franklin Ma, Freda Shi, and Jian Zhu. 2025. LingGym: How Far Are LLMs from Thinking Like Field Linguists?. In Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing, pages 1314–1340, Suzhou, China. Association for Computational Linguistics.