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Improving Amharic Information Retrieval with Translative and Multi-Agent Debate Retrieval Augmented Generation

Retrieval-augmented generation (RAG) has been used to improve the accuracy and transparency of outputs produced by large language models (LLMs) by integrating external knowledge; however, applying RAG to low-resource languages presents unique challenges, including poor embedding representations, low retrieval quality, and semantic gaps caused by the scarcity of digital documents. In this research, we address these challenges for a selected low-resource language, Amharic, by using translative and debate-based RAG techniques to improve retrieval and reasoning. This paper outlines the key problems and research gaps in applying RAG to low-resource languages and introduces a method to enhance RAG performance for Amharic. Additionally, we introduce the first comprehensive Amharic Retrieval-Augmented Generation Benchmark (ARGB), designed to capture grammatical, cultural, and writing-system-specific constraints of the Amharic language. ARGB evaluates not only retrieval and generation quality, but also noise robustness, counterfactual robustness, negative rejection, and multi-source information integration, providing a holistic assessment of RAG capabilities. The dataset, which spans a wide range of categories, is evaluated using multiple evaluation metrics. Furthermore, we demonstrate that, using our dataset, translation-based and debate-based methods substantially improve various aspects of RAG pipeline assessment in the Amharic language. This work aims to improve the reliability, accessibility, and inclusiveness of AI systems for Amharic speakers while providing a scalable framework for other low-resource languages. Current progress on the code and benchmark can be found on this GitHub link: link.