This research investigates the pragmatic competence of Large Language Models (LLMs) in interpreting implicit meanings within Italian political discourse. Using the IMPAQTS-PIDMM dataset, which is a multimodal benchmark derived from the 2.5-million-token IMPAQTS corpus, the experiment evaluates how effectively models identify tendentious content such as presuppositions and implicatures. The study compares the performance of text-only LLMs against speech-based models (SpeechLMs) that process both audio and transcriptions to determine if acoustic cues enhance understanding. The results reveal that text-only models significantly outperform multimodal variants, with Qwen2.5-72B achieving the highest global accuracy of 0.863. Surprisingly, the inclusion of audio did not improve performance, as SpeechLMs like GPT-4o-mini-audio-preview and Qwen2-Audio-7B-Instruct obtained lower accuracy scores and a higher frequency of missed answers compared to their text-only equivalents. Across all tested architectures, models generally demonstrated a superior ability to process presuppositions over implicatures.

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: https://github.com/WalterPaci/IMPAQTS-PID