| 研究実績の概要 |
The main research objective of this fiscal year is to develop and evaluate the proposed federated information retrieval framework that accounts for both privacy preservation and personalization. To this end, we firstly conducted a comprehensive comparison of GBDT models and the newly proposed neural tree ensembles for ranking. Then we evaluated the effectiveness of performing ranking with GBDT-like models in a federated manner. Moreover, we investigated how to use LLMs to enhance ranking performance by ensembling multiple LLMs in a voting manner. Furthermore, we developed a conversational information seeking system that accounts for personalized retrieval and response generation, and the evaluation over the TREC iKAT test collection demonstrates the superiority of the proposed framework.
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