通过实体链接增强教育平台的检索增强生成
Enhancing Retrieval-Augmented Generation with Entity Linking for Educational Platforms
AI总结：
本文提出ELERAG架构，通过实体链接增强事实信号，提升教育问答系统在意大利语领域的准确性，实验显示其在专业领域表现优异，但通用领域效果不如交叉编码器。
摘要：
在大型语言模型（LLMs）时代，检索增强生成（RAG）架构因其能够将语言生成与可靠知识源结合而受到广泛关注。尽管其有效，基于语义相似度的RAG系统在专业领域往往无法确保事实准确性，术语歧义会影响检索相关性。本文提出ELERAG，一种增强的RAG架构，整合了来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。系统包含基于Wikidata的实体链接模块，并实现了基于反向排名融合（RRF）的混合重排序策略。为了验证我们的方法，我们将其与标准基线和最先进的方法进行了比较，包括加权分数重排序、独立交叉编码器和结合RRF+交叉编码器的管道。实验在两个基准上进行：一个定制的学术数据集和标准SQuAD-it数据集。结果表明，在领域特定的上下文中，ELERAG在基线和交叉编码器配置上表现显著优于。相反，交叉编码器方法在通用领域数据集上表现最佳。这些发现提供了强有力的实验证据，突显了领域不匹配效应，强调了领域适应的混合策略在增强教育RAG系统事实精度中的重要性，而无需依赖计算成本高昂的模型。它们还展示了实体感知RAG系统在教育环境中的潜力，促进了适应性和可靠的AI辅导工具。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
