Improving Multilingual Retrieval-Augmented Language Models through Dialectic Reasoning Argumentations

Abstract

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 (DRAG), a modular approach guided by Argumentative Explanations, i.e., structured reasoning process that systematically evaluates retrieved information by comparing, contrasting, and resolving conflicting perspectives. Given a query and a set of multilingual related documents, D-RAG selects and exemplifies relevant knowledge for delivering dialectic explanations that, by critically weighing opposing arguments and filtering extraneous content, clearly determine the final response. We show the impact of our framework both as an in-context learning strategy and for constructing demonstrations to instruct smaller models. Our experiments demonstrate that D-RAG significantly improves RAG approaches, requiring low-impact computational effort and providing robustness to knowledge perturbations.