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Personalized conversational information retrieval (CIR) combines conversational and personalizable elements to satisfy various users' complex information needs through multi-turn interaction based on their backgrounds.
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ConvTrans: Transforming Web Search Sessions for Conversational Dense Retrieval. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing . 2935–2946
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AlpacaEval: An Automatic Evaluator of Instruction-following Models
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Pyserini: A Python toolkit for reproducible information retrieval research with sparse and dense representations. In Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval . 2356–2362
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Mohammad Aliannejadi, Zahra Abbasiantaeb, Shubham Chatterjee, Jeffery Dalton, and Leif Azzopardi. 2024 · 2024
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C-eval: A multi-level multi-discipline chinese evaluation suite for foundation models
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Kelong Mao, Chenlong Deng, Haonan Chen, Fengran Mo, Zheng Liu, Tetsuya Sakai, and Zhicheng Dou. 2024 · 2024
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CHIQ: Contextual History Enhancement for Improving Query Rewriting in Conversational Search
Fengran Mo, Abbas Ghaddar, Kelong Mao, Mehdi Rezagholizadeh, Boxing Chen, Qun Liu, and Jian-Yun Nie. 2024a · 2024
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History-Aware Conversational Dense Retrieval
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A User-Centric Benchmark for Evaluating Large Language Models
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