Improving Multilingual Retrieval-Augmented Language Models through Dialectic Reasoning Argumentations
This work addresses the problem of knowledge conflicts in multilingual RAG for language models, offering an incremental enhancement through structured reasoning.
The paper tackles the challenge of handling conflicting knowledge in multilingual retrieval-augmented generation (RAG) by introducing Dialectic-RAG (DRAG), a modular approach that uses argumentative explanations to evaluate and resolve retrieved information, resulting in significant improvements in RAG performance with low computational effort and robustness to knowledge perturbations.
Retrieval-augmented generation (RAG) is key to enhancing large language models (LLMs) to systematically access richer factual knowledge. Yet, using RAG brings intrinsic challenges, as LLMs must deal with potentially 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, DRAG selects and exemplifies relevant knowledge for delivering dialectic explanations that, by critically weighing opposing arguments and filtering extraneous content, clearly determine the final response. Through a series of in-depth experiments, we show the impact of our framework both as an in-context learning strategy and for constructing demonstrations to instruct smaller models. The final results demonstrate that DRAG significantly improves RAG approaches, requiring low-impact computational effort and providing robustness to knowledge perturbations.