In this paper, we explore physical commonsense reasoning of large language models (LLMs) and propose a specific methodology to evaluate low-level understanding of the physical world. Specifically, the goal is to create a test set to analyze physical commonsense reasoning in large language models for Italian and focus on a trustworthy analysis of the results. To that end, we present a tiered Italian dataset, called Graded Italian Annotated dataset (GITA), written and thoroughly annotated by a professional linguist, which allows us to concentrate on three different levels of commonsense understanding. Moreover, we create a semi-automated system to complete the accurate annotation of the dataset. We also validate our dataset by carrying out three tasks with a multilingual model (XLM-RoBERTa) and propose a qualitative analysis of the results. We found out that, although the model may perform at high-level classification tasks, its easoning is inconsistent and unverifiable, since it does not capture intermediate evidence.

Although large language models (LLMs) have apparently acquired a certain level of grammatical knowledge and the ability to make generalizations, they fail to interpret negation, a crucial step in Natural Language Processing.We try to clarify the reasons for the sub-optimal performance of LLMs understanding negation.We introduce a large semi-automatically generated dataset of circa 400,000 descriptive sentences about commonsense knowledge that can be true or false in which negation is present in about 2/3 of the corpus in different forms.We have used our dataset with the largest available open LLMs in a zero-shot approach to grasp their generalization and inference capability and we have also fine-tuned some of the models to assess whether the understanding of negation can be trained.Our findings show that, while LLMs are proficient at classifying affirmative sentences, they struggle with negative sentences and lack a deep understanding of negation, often relying on superficial cues.Although finetuning the models on negative sentences improves their performance, the lack of generalization in handling negation is persistent, highlighting the ongoing challenges of LLMs regarding negation understanding and generalization.The dataset and code are publicly available: https://github.com/hitz-zentroa/ This-is-not-a-Dataset