AI RESEARCH
The Impact of Ideological Discourses in RAG: A Case Study with COVID-19 Treatments
arXiv CS.CL
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ArXi:2603.14838v1 Announce Type: new This paper studies the impact of retrieved ideological texts on the outputs of large language models (LLMs). While interest in understanding ideology 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 ideological loaded texts about COVID-19 treatments. Our corpus is based on 1,117 academic articles representing dis.