They want to pretend not to understand: The Limits of Current LLM s in Interpreting Implicit Content of Political Discourse (2025)

Abstract: Implicit content plays a crucial role in political discourse, where systematically employ pragmatic strategies such as implicatures and presuppositions to influence their audiences. Large Language Models (LLMs) have demonstrated strong performance in tasks requiring complex semantic and pragmatic understanding, highlighting their potential for detecting and explaining the meaning of implicit content. However, their ability to do this within political discourse remains largely underexplored. Leveraging, for the very first time, the large IMPAQTS corpus comprising transcribed Italian political speeches with expert annotations of various types of implicit content, we propose methods to test the effectiveness of LLMs in this challenging problem. Through a multiple-choice task and an open-ended generation task, we demonstrate that all tested models struggle to interpret presuppositions and implicatures. To illustrate, the best-performing model provides a fully correct explanation in only one-fourth of cases in the open-ended generation setup. We conclude that current LLMs lack the key pragmatic capabilities necessary for accurately interpreting highly implicit language, such as that found in political discourse. At the same time, we highlight promising trends and future directions for enhancing model performance. We release our data and code at: \url{https://github.com/WalterPaci/IMPAQTS-PID}

## introduction implicit and vague language is pervasive in everyday life communication. consider the language used in political contexts: politicians often use implicit or vague content, relying on the listener's assumptions, background knowledge, and shared contextual knowledge to convey meaning indirectly. this is evident in the use of vague terms, allusions, and rhetorical devices that allow for multiple interpretations. such language creates an environment where meanings can be conveyed subtly, often with the intent of persuading, manipulating, or reinforcing certain ideologies @xcite . through the careful use of implicit language, politicians can appeal to the audience's emotions, assumptions, and cultural references, all while avoiding explicit commitments that could later be challenged or disproved @xcite ). consider the following example (also featured in the title of this work): they want to pretend not to understand. 1 by saying this, the speaker implies that the people being addressed pretend they do not understand something obvious, while, in reality, they understand it perfectly well; moreover, the speaker is also subtlety hinting that they are pretending so for a reason, guiding and manipulating the audience to reach a certain reaction or effect. this pragmatic phenomenon, where the speaker says one thing to imply another, is called an implicature. implicatures are among the most well-studied phenomena of implicit language @xcite , alongside presuppositions @xcite @xcite . as both are pervasive in political discourse, which makes it an ideal domain for analyzing implicit communication strategies @xcite . the advent of large language models (llms) has revolutionized nlp, bringing unprecedented progress to tasks involving semantic understanding @xcite , question answering @xcite , and pragmatic interpretation @xcite @xcite @xcite . overall, llms have been shown to perform excellently on semantic tasks and to exhibit potential in pragmatic tasks, particularly when they involve artificially constructed stimuli. how-ever, whether they can handle tasks that require making pragmatic inferences on naturalistic discourse data, such as political discourse, remains an open question. in this work, we specifically address this research question and test whether current llms can explain the meaning of manipulative implicit content in political discourse, such as the implicature in the example above. leveraging, for the first time in the context of nlp research, the large impaqts corpus @xcite , which includes transcribed italian political speeches with expert annotations of various types of implicit content, we propose methods to assess the pragmatic effectiveness of several llms, state-of-the-art for the italian language. our contributions are as follows: ## methods using the impaqts-pid dataset presented above, we challenge models to understand a sentence's implicit content. in this section, we describe the models we tested in our work, the experiments we performed to assess their abilities, and the experimental details common to both experiments. ## conclusions in this paper, we explored the ability of llms to explain implicit content in political discourse, focusing on implicatures and presuppositions. lever-aging, for the first time in the domain of nlp, the large-scale impaqts corpus containing italian political discourse, we evaluated several sota llms through two experiments. we showed that while model performance is not random, there is a significant gap between their outputs and human expert intuitions, both with predefined options and free-form explanations. we plan on designing a possibly more systematic way to generate robust distractors for the mcg task: something that can be considered is defining a set of rules to manipulate the correct choice (e.g., adding a negation, changing the quality of an adjective, etc.) and let a different llm generate the possible distractors according to these rules. we plan to explore the potential of this method in future work. we also found through a qualitative analysis that cot prompting can improve reasoning, confirming observations from previous work in the domain of pragmatic understanding @xcite . these findings show a limited pragmatic competence of current models when faced with highly implicit, contextdependent language such as that found in political speech. at the same time, our results offer promising directions for future work. we suggest that enriching llms with contextual and world knowledge such as information about the speaker, their political affiliations, or the historical setting of the speech could strengthen their interpretive capacities, a direction we plan to explore in future work.