Improving Multilingual Retrieval-Augmented Language Models through Dialectic Reasoning Argumentations
Year: 2025

Retrieval-augmented generation (RAG) is key to improving large language models (LLMs) in systematically accessing richer factual knowledge.Yet, using RAG mechanisms brings intrinsic challenges, as LLMs must deal with conflicting knowledge, especially in multilingual retrieval, where the heterogeneity of knowledge retrieved may deliver different outlooks.To make RAG more analytical, critical and grounded, we introduce Dialectic-RAG (D-RAG), a modular approach guided by Argumentative Explanations,