Wednesday 04 June 2025
Researchers have made significant progress in developing large language models (LLMs) that can effectively perform entity linking, a crucial task in natural language processing. Entity linking involves identifying and linking mentions of entities, such as people, places, or organizations, to their corresponding entries in a reference knowledge base.
Historically, entity linking has been a challenging task, particularly when dealing with long-tail entities – those that are less common or not well-represented in available databases. Traditional approaches have relied on rule-based methods or machine learning algorithms trained on large datasets, but these methods often struggle to accurately identify and link long-tail entities.
The recent study highlights the potential of LLMs, such as GPT-3.5 and LLama, in addressing this challenge. These models are trained on vast amounts of text data and have been shown to possess impressive language understanding capabilities. By leveraging these abilities, researchers were able to develop a simple yet effective entity linking approach using LLMs.
The approach involves prompting the LLMs with a sentence containing an entity mention, asking them to generate a JSON-style output that includes the identified entity along with its corresponding Wikipedia page title. The models are then evaluated on their ability to accurately link entities in a dataset of historical texts, known as MHERCL.
The results are impressive: LLMs outperform traditional approaches and even surpass state-of-the-art systems like ReLiK, a specialized tool for entity linking and relation extraction. When dealing with long-tail entities, the LLMs demonstrate a significant advantage, recovering a higher number of entities and achieving better recall scores compared to ReLiK.
The study also highlights the potential benefits of using LLMs in historical and domain-specific contexts, where traditional approaches may struggle due to limited data and knowledge. By leveraging the vast language understanding capabilities of LLMs, researchers can potentially fill the gap between frequent and infrequent entities.
While there is still room for improvement, the results are encouraging and suggest that LLMs could be a valuable tool in the field of entity linking. As the technology continues to evolve, we can expect to see even more impressive applications of large language models in natural language processing.
The study demonstrates the potential of LLMs in addressing the challenge of long-tail entities, and their ability to recover a higher number of entities with better recall scores compared to traditional approaches.
Cite this article: “Large Language Models Outperform Traditional Approaches in Entity Linking”, The Science Archive, 2025.
Language Models, Entity Linking, Natural Language Processing, Large Language Models, Entity Recognition, Long-Tail Entities, Wikipedia, Historical Texts, Mhercl, Recall Scores
