Empowering Low-Resource Languages: T ra S e Architecture for Enhanced Retrieval-Augmented Generation in B angla (2025)

Abstract: Research on Retrieval-Augmented Generation for low-resource languages has been sparse because of limited resources. To address this, we focus on Bangla, a low-resource language, and have created a dataset of 200 question-answer pairs as a basis for our study from Bangla Wikipedia dumps data. This paper introduces the TraSe architecture, which enhances RAG for Bangla using Translative prompting. Our experiments demonstrate that TraSe improves answer selection accuracy, achieving 34% with automatic retrieval and 63% with Human-in-the-Loop retrieval, outperforming baseline methods. The TraSe architecture marks a significant advancement in RAG for low-resource languages and has the potential to enhance question-answering systems for Bangla and similar languages. Future research could explore additional low-resource languages. The code is available at the following GitHub repository: https://github.com/Atia6/TraSe-Bangla-RAG.

## introduction the rapid advancements in natural language processing (nlp) have led to the development of sophisticated models that can perform a wide range of tasks with high accuracy @xcite . among these, retrieval-augmented generation (rag) has emerged as a powerful approach that combines the strengths of information retrieval and generative models to produce more informed and contextually accurate responses. while rag has been extensively explored in languages like english, its application in low-resource languages, such as bangla, remains significantly underdeveloped @xcite . the scarcity of research and resources in bangla rag presents a critical gap in the nlp field, particularly given the language's extensive use by over 230 million speakers worldwide @xcite . existing systems struggle to meet the nuanced demands of bangla language processing, often unable to retrieve @xcite and generate contextually relevant information effectively @xcite . this gap not only limits the practical applications of nlp in bangla but also highlights the need for tailored architectures to address the unique challenges posed by this language. in response to this need, we propose the trase architecture, a novel approach specifically designed for the rag in bangla. trase integrates advanced retrieval mechanisms with generative capabilities, optimizing performance across various tasks by leveraging both pre-existing knowledge and contextual information. this paper presents a detailed examination of trase's architecture, its comparative performance against existing systems, and its potential to enhance bangla language processing. through this research, we aim to contribute a significant step forward in the development of effective nlp tools for bangla, bridging the gap in rag research for this important language. ## related work retrieval-augmented generation (rag) has emerged as a powerful framework for addressing key limitations of large language models (llms), such as hallucination, outdated knowledge, and lack of transparency @xcite . by integrating external knowledge into the generation process, rag enhances accuracy, reliability, and contextual relevance @xcite . over time, the paradigm has evolved from simple retrieval-based augmentation to more sophisticated modular architectures that optimize retrieval, generation, and augmentation processes @xcite . a notable advancement in this direction is flare, an active retrieval mechanism that continuously gathers relevant information throughout the generation process to improve response quality @xcite . beyond traditional text-based applications, rag has demonstrated versatility across multimodal tasks and knowledge-intensive scenarios, reinforcing its potential in various domains @xcite . despite these advancements, rag still faces challenges in evaluation, retrieval quality, and real-world implementation. researchers are actively working to develop comprehensive benchmarks and refine methodologies to improve retrieval accuracy, optimize integration with llms, and enhance system adaptability @xcite . several recent innovations have focused on addressing these limitations. corrective rag, introduced by @xcite , incorporates a retrieval evaluator to assess document quality and dynamically trigger different retrieval actions, such as web searches, thereby improving the reliability of retrieved content. selfmem @xcite takes a different approach by iteratively using a retrieval-augmented generator to build an unbounded memory pool, leveraging past model outputs as a self-referential knowledge base. meanwhile, iter-retgen @xcite adopts an iterative retrieval-generation cycle where model-generated content informs subsequent retrieval steps, refining relevance and coherence. these methods specifically address issues related to retrieval precision, fixed corpus constraints, and complex information needs, demonstrating improved performance across various nlp tasks, including question answering, summarization, and dialogue generation. further developments continue to push the boundaries of rag optimization. stochastic rag @xcite introduces an end-to-end optimization framework that utilizes straight-through gumbel-top-k selection, enhancing retrieval and generation efficiency while achieving state-of-the-art results across multiple tasks. blended rag @xcite improves retrieval effectiveness by leveraging hybrid query strategies and semantic search, surpassing conventional fine-tuning approaches on datasets like squad. additionally, graph retrieval-augmented generation (grag) @xcite presents a divide-and-conquer strategy for retrieving structured textual subgraphs, facilitating multi-hop reasoning, and significantly outperforming standard rag models in handling networked document structures. beyond these techniques, other research efforts have sought to refine rag's adaptability and evaluation. r^2ag @xcite aims to bridge the semantic gap between retrievers and llms by embedding retrieval information directly into the generation process. ragas @xcite introduces a referencefree evaluation framework to assess retrieval relevance, llm faithfulness, and overall generation quality, providing a more holistic assessment of rag pipelines. the ragged framework @xcite analyzes different rag configurations, revealing that optimal performance depends on varying model architectures and context utilization strategies. additionally, memorag @xcite pioneers a memory-augmented approach that employs a dual-system architecture-where a lightweight llm manages global memory while a more expressive llm handles final answer generation-enabling better handling of ambiguous queries and long-term knowledge retention. together, these advancements illustrate the increasing sophistication of rag techniques and their transformative potential for llms. by improving retrieval strategies, optimizing generative integration, and expanding to new application areas, rag continues to evolve as a fundamental enabler of more accurate, contextually aware, and reliable ai-generated content. ## methodology in this study, we developed trase architecture, a selection-based process to improve the performance of rag for bangla question answering with the help of the translative method. we further compared the performance of our model with existing techniques. ## result and discussion the efficiency of the translative method for text-based question answering is evident in figure 4 . with an accuracy of 0.28 for banglabert, 0.24 for bert-base-multilingualcase, and 0.61 for the hil context, this method consistently outperforms the other four methods for text-based answering. additionally, the translative method demonstrates competitive accuracy in number-based answers. ## conclusion in this study, we introduced the translative prompting model, which demonstrated strong performance in both number-based and textbased answers for bangla rag. building on this, we developed the trase model, leveraging the strengths of translative prompting to enhance answer selection from previously generated responses. the trase model achieved notable accuracy improvements, reaching 34% accuracy with automatic retrieval and 63% accuracy with human-in-the-loop (hil) retrieval, underscoring its effectiveness in both automated and human-assisted retrieval contexts. future research should prioritize incorporating a variety of language models, larger and more diverse datasets, and an expanded set of low-resource languages to validate and build upon these findings, ultimately contributing to a deeper and more generalizable understanding of language model performance.