2023 Fiscal Year Annual Research Report
A scalable privacy-preserving information retrieval system based on federated optimization, on-device intelligence and semantic matching
| Project/Area Number |
19H04215
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| Research Institution | University of Tsukuba |
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Principal Investigator |
于 海涛 筑波大学, 図書館情報メディア系, 准教授 (30751052)
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| Co-Investigator(Kenkyū-buntansha) |
吉川 正俊 大阪成蹊大学, データサイエンス学部, 教授 (30182736)
康 シン 徳島大学, 大学院社会産業理工学研究部(理工学域), 助教 (80777350)
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| Project Period (FY) |
2019-04-01 – 2024-03-31
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| Keywords | Federated Learning / Neural Tree Ensembles / Large Language Model / Generative IR |
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| Outline of Annual Research Achievements |
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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| Research Progress Status |
令和5年度が最終年度であるため、記入しない。
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| Strategy for Future Research Activity |
令和5年度が最終年度であるため、記入しない。
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| Remarks |
(1) Ptranking includes the source codes of a number of published methods that are supported by this project. (2) PPIR shows the main research achievements of this project.
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Research Products
(13 results)
