Understanding pragmatics-the use of language in context-is crucial for developing NLP systems capable of interpreting nuanced language use. Despite recent advances in language technologies, including large language models, evaluating their ability to handle pragmatic phenomena such as implicatures and references remains challenging. To advance pragmatic abilities in models, it is essential to understand current evaluation trends and identify existing limitations. In this survey, we provide a comprehensive review of resources designed for evaluating pragmatic capabilities in NLP, categorizing datasets by the pragmatic phenomena they address. We analyze task designs, data collection methods, evaluation approaches, and their relevance to real-world applications. By examining these resources in the context of modern language models, we highlight emerging trends, challenges, and gaps in existing benchmarks. Our survey aims to clarify the landscape of pragmatic evaluation and guide the development of more comprehensive and targeted benchmarks, ultimately contributing to more nuanced and context-aware NLP models.
View on arXiv@article{ma2025_2502.12378,
title={ Pragmatics in the Era of Large Language Models: A Survey on Datasets, Evaluation, Opportunities and Challenges },
author={ Bolei Ma and Yuting Li and Wei Zhou and Ziwei Gong and Yang Janet Liu and Katja Jasinskaja and Annemarie Friedrich and Julia Hirschberg and Frauke Kreuter and Barbara Plank },
journal={arXiv preprint arXiv:2502.12378},
year={ 2025 }
}