This paper studies the impact of retrieved ideologically framed texts on the outputs of large language models (LLMs). While interest in understanding ideological framing in LLMs has recently increased, little attention has been given to this issue in the context of Retrieval-Augmented Generation (RAG). To fill this gap, we design an external knowledge source based on ideologically framed texts about COVID-19 treatments. Our corpus is based on 1,117 academic articles representing discourses about controversial and endorsed treatments for the disease. We propose a corpus linguistics framework, based on Lexical Multidimensional Analysis (LMDA), to identify discourse dimensions within the corpus. LLMs are tasked to answer questions derived from three identified discourse dimensions, and two types of contextual prompts are adopted: the first comprises the user question and ideologically framed texts; and the second contains the question, ideologically framed texts, and LMDA descriptions. Alignment between reference ideologically framed texts and LLMs’ responses is assessed using cosine similarity for lexical and semantic representations. Results demonstrate that retrieved ideologically framed texts influence LLM responses toward the discourse framing represented in the external knowledge, with enhanced prompts further amplifying this effect. Our findings highlight the importance of identifying ideological framings within the RAG framework in order to mitigate not just unintended ideological bias, but also the risks of intentional discourse steering of such models.

The growing sophistication of speech generated by Artificial Intelligence (AI) has introduced new challenges in audio deepfake detection. Text-to-speech (TTS) and voice conversion (VC) technologies can create highly convincing synthetic speech with naturalness and intelligibility. This poses serious threats to voice biometric security and to systems designed to combat the spread of spoken misinformation, where synthetic voices may be used to disseminate false or malicious content. While interest in AI-generated speech has increased, resources for evaluating naturalness at the phoneme level remain limited. In this work, we address this gap by presenting the Phoneme-Level DeepFake dataset (PhonemeDF), comprising parallel real and synthetic speech segmented at the phoneme level. Real speech samples are derived from a subset of LibriSpeech, while synthetic samples are generated using four TTS and three VC systems. For each system, phoneme-aligned TextGrid files are obtained using the Montreal Forced Aligner (MFA). We compute the Kullback–Leibler divergence (KLD) between real and synthetic phoneme distributions to quantify fidelity and establish a ranking based on similarity to natural speech. Our findings show a clear correlation between the KLD of real and synthetic phoneme distributions and the performance of classifiers trained to distinguish them, suggesting that KLD can serve as an indicator of the most discriminative phonemes for deepfake detection.