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

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 FewShot 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 GPT4o show unexpected limitations compared to significantly smaller open-source LLMs. Next, we thoroughly analyze the effect of LLM FineTuning, 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.