Enhancing Retrieval-Augmented Generation with Entity Linking for Educational Platforms

TL;DR

This paper introduces ELERAG, an improved retrieval-augmented generation system for educational question-answering in Italian, integrating entity linking and hybrid re-ranking to enhance factual accuracy in domain-specific contexts.

Contribution

The study presents a novel hybrid RAG architecture that combines entity linking and re-ranking strategies, demonstrating improved factual accuracy over standard methods in educational domains.

Findings

ELERAG outperforms baseline models in domain-specific datasets.

Cross-Encoder methods excel in general-domain datasets.

Hybrid strategies effectively address domain mismatch issues.

Abstract

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.