HybridRAG-BN: A Retrieval-Augmented Framework with Fine-Tuned Verification for Bangla KBQA
Summary
This paper proposes HybridRAG-BN, a retrieval-augmented framework for Bangla knowledge-base question answering that combines hybrid retrieval, Gemma-based generation, and LoRA fine-tuned verification, achieving first place with F1 scores of 0.71654 and 0.72912.
View Cached Full Text
Cached at: 08/14/26, 09:28 AM
# HybridRAG-BN: A Retrieval-Augmented Framework with Fine-Tuned Verification for Bangla KBQA Source: [https://arxiv.org/abs/2608.13004](https://arxiv.org/abs/2608.13004) [View PDF](https://arxiv.org/pdf/2608.13004) > Abstract:Knowledge\-base question answering \(KBQA\) systems rely on effective retrieval and reasoning mechanisms to generate accurate answers from external knowledge sources\. However, developing reliable KBQA systems for low\-resource languages such as Bangla remains challenging due to limited retrieval\-focused research, scarce language resources, and difficulties in grounding generated responses in external knowledge\. In this work, we propose HybridRAG\-BN, a retrieval\-augmented framework for Bangla KBQA that integrates hybrid retrieval using BM25 and BGE\-M3, answer generation using the GGUF version of Gemma\-4\-31B\-Instruct, and a LoRA\-fine\-tuned Gemma\-4\-31B\-Instruct model for answer verification and refinement\. To further improve robustness, the framework incorporates a post\-processing stage that addresses unresolved cases through fallback answer replacement and DuckDuckGo\-assisted retrieval\. Experimental results demonstrate the effectiveness of the proposed framework, achieving token\-level F1 scores of 0\.71654 and 0\.72912 on the public and private leaderboards, respectively, securing first place in the competition\. ## Submission history From: Nirjhar Das \[[view email](https://arxiv.org/show-email/c2c0315a/2608.13004)\] **\[v1\]**Thu, 13 Aug 2026 09:24:48 UTC \(534 KB\)
Similar Articles
BengaliMCQ: Automatic Generation and Answer Prediction of Academic Multiple-Choice Questions in a Low-Resource Language
BengaliMCQ is a structure-aware RAG framework using graph neural networks to model hierarchical document structures in Bengali textbooks, enabling automatic generation and answer prediction of academic multiple-choice questions with improved performance over baseline methods.
AB-RAG: Adaptive Budgeted Retrieval-Augmented Generation for Reliable Question Answering
AB-RAG is a training-free, backbone-agnostic framework that adaptively retrieves passages for question answering by estimating answer confidence, improving efficiency and accuracy across multiple backbones and datasets.
RAGA: Reading-And-Graph-building-Agent for Autonomous Knowledge Graph Construction and Retrieval-Augmented Generation
RAGA is an LLM-driven autonomous agent that constructs knowledge graphs via a read-search-verify-construct cognitive loop and integrates hybrid symbolic-vector retrieval for retrieval-augmented generation, with experimental gains on scientific QA datasets.
When Retrieval Doesn't Help: A Large-Scale Study of Biomedical RAG
A large-scale study across 5 models (7B–72B), 10 biomedical QA datasets, 4 retrieval methods, and 4 corpora finds that RAG yields only small and inconsistent gains (1–2 points) over no-retrieval baselines in biomedical question answering. The study concludes that the main bottleneck is not retrieval quality but models' limited ability to effectively use retrieved evidence.
MM-BizRAG: Rethinking Multimodal Retrieval-Augmented Generation for General Purpose Enterprise Q&A
MM-BizRAG is a multimodal retrieval-augmented generation system for enterprise Q&A that uses document structure-aware splitting and layout-aware parsing to outperform vision-centric baselines by up to 32% on heterogeneous enterprise documents. The paper also introduces FastRAGEval, a cost-efficient LLM-based evaluation metric with stronger human alignment than RAGChecker.