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| Main Authors: | , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2509.15588 |
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| _version_ | 1866909797515788288 |
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| author | Chang, Yu-Cheng Yeo, Guan-Wei Eugene, Quah Shih, Fan-Jie Kuo, Yuan-Ching Yu, Tsung-En Hsu, Hung-Chun Tsai, Ming-Feng Wang, Chuan-Ju |
| author_facet | Chang, Yu-Cheng Yeo, Guan-Wei Eugene, Quah Shih, Fan-Jie Kuo, Yuan-Ching Yu, Tsung-En Hsu, Hung-Chun Tsai, Ming-Feng Wang, Chuan-Ju |
| contents | The 2025 TREC Interactive Knowledge Assistance Track (iKAT) featured both interactive and offline submission tasks. The former requires systems to operate under real-time constraints, making robustness and efficiency as important as accuracy, while the latter enables controlled evaluation of passage ranking and response generation with pre-defined datasets. To address this, we explored query rewriting and retrieval fusion as core strategies. We built our pipelines around Best-of-$N$ selection and Reciprocal Rank Fusion (RRF) strategies to handle different submission tasks. Results show that reranking and fusion improve robustness while revealing trade-offs between effectiveness and efficiency across both tasks. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_15588 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | CFDA & CLIP at TREC iKAT 2025: Enhancing Personalized Conversational Search via Query Reformulation and Rank Fusion Chang, Yu-Cheng Yeo, Guan-Wei Eugene, Quah Shih, Fan-Jie Kuo, Yuan-Ching Yu, Tsung-En Hsu, Hung-Chun Tsai, Ming-Feng Wang, Chuan-Ju Information Retrieval Artificial Intelligence The 2025 TREC Interactive Knowledge Assistance Track (iKAT) featured both interactive and offline submission tasks. The former requires systems to operate under real-time constraints, making robustness and efficiency as important as accuracy, while the latter enables controlled evaluation of passage ranking and response generation with pre-defined datasets. To address this, we explored query rewriting and retrieval fusion as core strategies. We built our pipelines around Best-of-$N$ selection and Reciprocal Rank Fusion (RRF) strategies to handle different submission tasks. Results show that reranking and fusion improve robustness while revealing trade-offs between effectiveness and efficiency across both tasks. |
| title | CFDA & CLIP at TREC iKAT 2025: Enhancing Personalized Conversational Search via Query Reformulation and Rank Fusion |
| topic | Information Retrieval Artificial Intelligence |
| url | https://arxiv.org/abs/2509.15588 |