@inproceedings{sravanthi-etal-2025-understand,
title = "Understand the Implication: Learning to Think for Pragmatic Understanding",
author = "Sravanthi, Settaluri Lakshmi and
Maharaj, Kishan and
Gunnu, Sravani and
Mishra, Abhijit and
Bhattacharyya, Pushpak",
editor = "Che, Wanxiang and
Nabende, Joyce and
Shutova, Ekaterina and
Pilehvar, Mohammad Taher",
booktitle = "Findings of the Association for Computational Linguistics: ACL 2025",
month = jul,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.findings-acl.1218/",
doi = "10.18653/v1/2025.findings-acl.1218",
pages = "23778--23790",
ISBN = "979-8-89176-256-5",
abstract = "Pragmatics, the ability to infer meaning beyond literal interpretation, is crucial for social cognition and communication. While LLMs have been benchmarked for their pragmatic understanding, improving their performance remains underexplored. Existing methods rely on annotated labels but overlook the reasoning process humans naturally use to interpret implicit meaning. To bridge this gap, we introduce a novel pragmatic dataset \textbf{ImpliedMeaningPreference} that includes \textit{explicit reasoning ({`}thoughts')} for both correct and incorrect interpretations. Through preference-tuning and supervised fine-tuning, we demonstrate that thought-based learning significantly enhances LLMs' pragmatic understanding, improving accuracy by 11.12{\%} across model families. We further discuss a transfer-learning study where we evaluate the performance of \textit{thought}-based training for the other tasks of pragmatics (presupposition, deixis) that are not seen during the training time and observe an improvement of 16.10{\%} compared to \textit{label} trained models."
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<abstract>Pragmatics, the ability to infer meaning beyond literal interpretation, is crucial for social cognition and communication. While LLMs have been benchmarked for their pragmatic understanding, improving their performance remains underexplored. Existing methods rely on annotated labels but overlook the reasoning process humans naturally use to interpret implicit meaning. To bridge this gap, we introduce a novel pragmatic dataset ImpliedMeaningPreference that includes explicit reasoning (‘thoughts’) for both correct and incorrect interpretations. Through preference-tuning and supervised fine-tuning, we demonstrate that thought-based learning significantly enhances LLMs’ pragmatic understanding, improving accuracy by 11.12% across model families. We further discuss a transfer-learning study where we evaluate the performance of thought-based training for the other tasks of pragmatics (presupposition, deixis) that are not seen during the training time and observe an improvement of 16.10% compared to label trained models.</abstract>
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%0 Conference Proceedings
%T Understand the Implication: Learning to Think for Pragmatic Understanding
%A Sravanthi, Settaluri Lakshmi
%A Maharaj, Kishan
%A Gunnu, Sravani
%A Mishra, Abhijit
%A Bhattacharyya, Pushpak
%Y Che, Wanxiang
%Y Nabende, Joyce
%Y Shutova, Ekaterina
%Y Pilehvar, Mohammad Taher
%S Findings of the Association for Computational Linguistics: ACL 2025
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-256-5
%F sravanthi-etal-2025-understand
%X Pragmatics, the ability to infer meaning beyond literal interpretation, is crucial for social cognition and communication. While LLMs have been benchmarked for their pragmatic understanding, improving their performance remains underexplored. Existing methods rely on annotated labels but overlook the reasoning process humans naturally use to interpret implicit meaning. To bridge this gap, we introduce a novel pragmatic dataset ImpliedMeaningPreference that includes explicit reasoning (‘thoughts’) for both correct and incorrect interpretations. Through preference-tuning and supervised fine-tuning, we demonstrate that thought-based learning significantly enhances LLMs’ pragmatic understanding, improving accuracy by 11.12% across model families. We further discuss a transfer-learning study where we evaluate the performance of thought-based training for the other tasks of pragmatics (presupposition, deixis) that are not seen during the training time and observe an improvement of 16.10% compared to label trained models.
%R 10.18653/v1/2025.findings-acl.1218
%U https://aclanthology.org/2025.findings-acl.1218/
%U https://doi.org/10.18653/v1/2025.findings-acl.1218
%P 23778-23790
Markdown (Informal)
[Understand the Implication: Learning to Think for Pragmatic Understanding](https://aclanthology.org/2025.findings-acl.1218/) (Sravanthi et al., Findings 2025)
ACL