It is not a piece of cake for GPT: Explaining Textual Entailment Recognition in the presence of Figurative Language

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F26%3AI7D4RGJG" target="_blank" >RIV/00216208:11320/26:I7D4RGJG - isvavai.cz</a>

Result on the web

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DOI - Digital Object Identifier

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Result language

angličtina

Original language name

It is not a piece of cake for GPT: Explaining Textual Entailment Recognition in the presence of Figurative Language

Original language description

Textual Entailment Recognition (TER) aims to predict whether a pair of premise-hypothesis sentences represents an entailment, a contradiction, or none of the above. Addressing TER in the presence of figurative language is particularly challenging because words are used in a way that deviates from the conventional order and meaning. In this work, we investigate the capabilities of Large Language Models (LLMs) to address TER and generate textual explanations of TER predictions. First, we evaluate LLM performance in Zero- and Few-Shot Learning settings, with and without using Chain-of-Thought prompting. After identifying the best prompts, we highlight the settings in which in-context learning is beneficial. The closed-source models GPT-3.5 Turbo and GPT-4o show unexpected limitations compared to significantly smaller open-source LLMs. Next, we thoroughly analyze the effect of LLM Fine-Tuning, showing substantial improvements in the quality of TER explanations compared to Zero- and Few-Shot Learning. Notably, 9 billion parameter open-source LLMs demonstrate again competitive performance against larger closed-source models. Finally, we compare our LLM-based approach with the state-of-the-art DREAM-FLUTE and Cross-Task architectures. The results show significant performance improvements, particularly in the quality of the generated explanations. © 2025 Association for Computational Linguistics.

Czech name

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Czech description

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Type

D - Article in proceedings

CEP classification

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OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

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Continuities

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Publication year

2025

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection

Proc. Main Conf. Int. Conf. Comput. Linguist., COLING

ISBN

979-8-89176-196-4

ISSN

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e-ISSN

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Number of pages

19

Pages from-to

9656-9674

Publisher name

Association for Computational Linguistics (ACL)

Place of publication

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Event location

Abu Dhabi

Event date

Jan 1, 2026

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

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