Evaluation for Large Language Models in Entity Search
Entity search,which is a critical task in information retrieval,aims to accurately identify target entities to a user query from a vast collection of documents.It plays a key role in enhancing user experience,enabling cross-domain applications,facilitating big data analysis,and supporting intelligent services.With the development of large language models(LLMs),they have demonstrated outstanding performance across various fields.The powerful capabilities of semantic understanding and generation of LLMs can significantly improve the accuracy of entity search.However,the evaluation of LLMs specifically for entity search tasks has not yet been fully explored.Therefore,an evaluation framework for LLMs tailored to entity search tasks are proposed,which can not only improve the evaluation framework but also provide valuable insights for further optimization and application of these models.By constructing and publicly releasing a cross-domain Chinese entity search test set,nine open-source LLMs are tested and their practical performance in entity search is demonstrated.Through comparative experiments,the performance of LLMs are evaluated and analyzed from multiple perspectives,providing empirical evidence for their application in the entity search domain and offering new insights for future research.
万方数据
维普
