混合检索+精调验证，提升孟加拉语知识问答准确率
HybridRAG-BN: A Retrieval-Augmented Framework with Fine-Tuned Verification for Bangla KBQA
- 融合BM25与BGE-M3进行混合检索，提升答案相关性
- 使用微调模型验证并优化生成答案，提升准确率至F1 0.729
- 适合低资源语言问答研究者参考，尤其关注孟加拉语
面向低资源语言孟加拉语的知识库问答系统面临检索不足、资源匮乏及答案难以对齐外部知识等挑战。本文提出HybridRAG-BN框架，结合BM25与BGE-M3的混合检索机制，采用GGUF格式的Gemma-4-31B-Instruct生成答案，并通过LoRA微调的Gemma-4-31B-Instruct模型进行答案验证与修正。此外，引入后处理阶段，对未解决案例采用兜底替换与DuckDuckGo辅助检索增强鲁棒性。实验表明，该框架在公开与私有榜单上分别取得0.71654与0.72912的分词级F1分数，位居竞赛首位。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。