In the era of Large Language Models (LLMs), Retrieval-Augmented Generation (RAG) architectures are gaining significant attention for their ability to ground language generation in reliable knowledge sources. Despite their effectiveness, RAG systems based solely on semantic similarity often fail to ensure factual accuracy in specialized domains, where terminological ambiguity can affect retrieval relevance. This study proposes ELERAG, an enhanced RAG architecture that integrates a factual signal derived from Entity Linking to improve the accuracy of educational question-answering systems in Italian. The system includes a Wikidata-based Entity Linking module and implements a hybrid re-ranking strategy based on Reciprocal Rank Fusion (RRF). To validate our approach, we compared it against standard baselines and state-of-the-art methods, including a Weighted-Score Re-ranking, a standalone Cross-Encoder and a combined RRF+Cross-Encoder pipeline. Experiments were conducted on two benchmarks: a custom academic dataset and the standard SQuAD-it dataset. Results show that, in domain-specific contexts, ELERAG significantly outperforms both the baseline and the Cross-Encoder configurations. Conversely, the Cross-Encoder approaches achieve the best results on the general-domain dataset. These findings provide strong experimental evidence of the domain mismatch effect, highlighting the importance of domain-adapted hybrid strategies to enhance factual precision in educational RAG systems without relying on computationally expensive models trained on disparate data distributions. They also demonstrate the potential of entity-aware RAG systems in educational environments, fostering adaptive and reliable AI-based tutoring tools.

翻译：在大语言模型（LLM）时代，检索增强生成（RAG）架构因其能够将语言生成建立在可靠知识源上而受到广泛关注。尽管RAG系统效果显著，但仅基于语义相似性的方法在专业领域中往往难以保证事实准确性，其中术语歧义会影响检索的相关性。本研究提出ELERAG，一种增强的RAG架构，通过整合源自实体链接的事实信号，以提高意大利语教育问答系统的准确性。该系统包含基于Wikidata的实体链接模块，并采用基于互逆排序融合（RRF）的混合重排序策略。为验证所提方法，我们将其与标准基线及前沿方法进行比较，包括加权分数重排序、独立交叉编码器以及RRF+交叉编码器组合流程。实验在两个基准数据集上进行：自定义学术数据集和标准SQuAD-it数据集。结果表明，在特定领域场景中，ELERAG显著优于基线及交叉编码器配置；反之，交叉编码器方法在通用领域数据集上取得最佳结果。这些发现为领域失配效应提供了有力的实验证据，凸显了领域自适应混合策略对于提升教育RAG系统事实精确度的重要性，且无需依赖在不同数据分布上训练的高计算成本模型。研究同时证明了实体感知RAG系统在教育环境中的潜力，有助于开发自适应且可靠的基于人工智能的辅导工具。