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Despite its substantial impact on various search, recommendation, and question answering tasks, privacy-preserving methods for personalizing large language models (LLMs) have received relatively limited exploration.
Deep Learning Recommendation Model for Personalization and Recommendation Systems
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The Personalization of Conversational Agents in Health Care: Systematic Review
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Retrieval-augmented generation for knowledge-intensive NLP tasks. In Proceedings of the 34th International Conference on Neural Information Processing Systems (<conf-loc>, <city>Vancouver</city>, <state>BC</state>, <country>Canada</country>, </conf-loc>) (NIPS ’20) . Curran Associates Inc., Red Hook, NY, USA, Article 793, 16 pages
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Personalized Search-based Query Rewrite System for Conversational AI. In Proceedings of the 3rd Workshop on Natural Language Processing for Conversational AI , Alexandros Papangelis, Paweł Budzianowski, Bing Liu, Elnaz Nouri, Abhinav Rastogi, and Yun-Nung Chen (Eds.). Association for Computational Linguistics, Online, 179–188
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AOL4PS: A Large-scale Data Set for Personalized Search
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Leveraging Passage Retrieval with Generative Models for Open Domain Question Answering. In Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume , Paola Merlo, Jorg Tiedemann, and Reut Tsarfaty (Eds.). Association for Computational Linguistics, Online, 874–880
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Pre-Training Multi-Modal Dense Retrievers for Outside-Knowledge Visual Question Answering. In Proceedings of the 2023 ACM SIGIR International Conference on Theory of Information Retrieval (<conf-loc>, <city>Taipei</city>, <country>Taiwan</country>, </conf-loc>) (ICTIR ’23) . Association for Computing Machinery, New York, NY, USA, 169–176
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Improving the Domain Adaptation of Retrieval Augmented Generation (RAG) Models for Open Domain Question Answering
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LoRA-FA: Memory-efficient Low-rank Adaptation for Large Language Models Fine-tuning
Longteng Zhang, Lin Zhang, Shaohuai Shi, Xiaowen Chu, and Bo Li. 2023 · 2023
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KILT: a Benchmark for Knowledge Intensive Language Tasks. In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies , Kristina Toutanova, Anna Rumshisky, Luke Zettlemoyer, Dilek Hakkani-Tur, Iz Beltagy, Steven Bethard, Ryan Cotterell, Tanmoy Chakraborty, and Yichao Zhou (Eds.). Association for Computational Linguistics, Online, 2523–2544
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Retrieval Augmentation Reduces Hallucination in Conversation. In Findings of the Association for Computational Linguistics: EMNLP 2021 , Marie-Francine Moens, Xuanjing Huang, Lucia Specia, and Scott Wen-tau Yih (Eds.). Association for Computational Linguistics, Punta Cana, Dominican Republic, 3784–3803
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MuRAG: Multimodal Retrieval-Augmented Generator for Open Question Answering over Images and Text. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing , Yoav Goldberg, Zornitsa Kozareva, and Yue Zhang (Eds.). Association for Computational Linguistics, Abu Dhabi, United Arab Emirates, 5558–5570
Wenhu Chen, Hexiang Hu, Xi Chen, Pat Verga, and William Cohen. 2022 · 2022
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Measuring the Carbon Intensity of AI in Cloud Instances. In Proceedings of the 2022 ACM Conference on Fairness, Accountability, and Transparency (Seoul, Republic of Korea) (FAccT ’22) . Association for Computing Machinery, New York, NY, USA, 1877–1894
Jesse Dodge, Taylor Prewitt, Remi Tachet des Combes, Erika Odmark, Roy Schwartz, Emma Strubell, Alexandra Sasha Luccioni, Noah A. Smith, Nicole DeCario, and Will Buchanan. 2022 · 2022
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LoRA: Low-Rank Adaptation of Large Language Models. In International Conference on Learning Representations
Edward J Hu, yelong shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, and Weizhu Chen. 2022 · 2022
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Unsupervised Dense Information Retrieval with Contrastive Learning
Gautier Izacard, Mathilde Caron, Lucas Hosseini, Sebastian Riedel, Piotr Bojanowski, Armand Joulin, and Edouard Grave. 2022 · 2022
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Retrieval-Enhanced Machine Learning. In Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval (<conf-loc>, <city>Madrid</city>, <country>Spain</country>, </conf-loc>) (SIGIR ’22) . Association for Computing Machinery, New York, NY, USA, 2875–2886
Hamed Zamani, Fernando Diaz, Mostafa Dehghani, Donald Metzler, and Michael Bendersky. 2022 · 2022
Cited alongside, same era.
A Survey on Large Language Models for Personalized and Explainable Recommendations
Junyi Chen. 2023 · 2023
Cited alongside, same era.
Tutorial on Large Language Models for Recommendation. In Proceedings of the 17th ACM Conference on Recommender Systems . 1281–1283
Wenyue Hua, Lei Li, Shuyuan Xu, Li Chen, and Yongfeng Zhang. 2023 · 2023
Cited alongside, same era.
Bashar Alhafni, Vivek Kulkarni, Dhruv Kumar, and Vipul Raheja. 2024 · 2024
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Self-RAG: Learning to Retrieve, Generate, and Critique through Self-Reflection. In The Twelfth International Conference on Learning Representations
Akari Asai, Zeqiu Wu, Yizhong Wang, Avirup Sil, and Hannaneh Hajishirzi. 2024 · 2024
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Scaling Instruction-Finetuned Language Models
Hyung Won Chung, Le Hou, Shayne Longpre, Barret Zoph, Yi Tay, William Fedus, Yunxuan Li, Xuezhi Wang, Mostafa Dehghani, Siddhartha Brahma, Albert Webson, Shixiang Shane Gu, Zhuyun Dai, Mirac Suzgun, Xinyun Chen, Aakanksha Chowdhery, Alex Castro-Ros, Marie Pellat, Kevin Robinson, Dasha Valter, Sharan Narang, Gaurav Mishra, Adams Yu, Vincent Zhao, Yanping Huang, Andrew Dai, Hongkun Yu, Slav Petrov, Ed H. Chi, Jeff Dean, Jacob Devlin, Adam Roberts, Denny Zhou, Quoc V. Le, and Jason Wei. 2024 · 2024
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Parameter-Efficient Fine-Tuning for Large Models: A Comprehensive Survey
Zeyu Han, Chao Gao, Jinyang Liu, Jeff Zhang, and Sai Qian Zhang. 2024 · 2024
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SE-PQA: Personalized Community Question Answering. In Companion Proceedings of the ACM Web Conference 2024 (Singapore, Singapore) (WWW ’24) . Association for Computing Machinery, New York, NY, USA, 1095–1098
Pranav Kasela, Marco Braga, Gabriella Pasi, and Raffaele Perego. 2024 · 2024
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LongLaMP: A Benchmark for Personalized Long-form Text Generation
Ishita Kumar, Snigdha Viswanathan, Sushrita Yerra, Alireza Salemi, Ryan A. Rossi, Franck Dernoncourt, Hanieh Deilamsalehy, Xiang Chen, Ruiyi Zhang, Shubham Agarwal, Nedim Lipka, and Hamed Zamani. 2024 · 2024
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Learning to Rewrite Prompts for Personalized Text Generation. In Proceedings of the ACM on Web Conference 2024 (WWW ’24) . ACM
Cheng Li, Mingyang Zhang, Qiaozhu Mei, Weize Kong, and Michael Bendersky. 2024c · 2024
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ReLoRA: High-Rank Training Through Low-Rank Updates. In The Twelfth International Conference on Learning Representations
Vladislav Lialin, Sherin Muckatira, Namrata Shivagunde, and Anna Rumshisky. 2024 · 2024
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Zhuoran Lu, Sheshera Mysore, Tara Safavi, Jennifer Neville, Longqi Yang, and Mengting Wan. 2024 · 2024
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LISA: Layerwise Importance Sampling for Memory-Efficient Large Language Model Fine-Tuning. In The Thirty-eighth Annual Conference on Neural Information Processing Systems
Rui Pan, Xiang Liu, Shizhe Diao, Renjie Pi, Jipeng Zhang, Chi Han, and Tong Zhang. 2024 · 2024
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LaMP: When Large Language Models Meet Personalization. In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) , Lun-Wei Ku, Andre Martins, and Vivek Srikumar (Eds.). Association for Computational Linguistics, Bangkok, Thailand, 7370–7392
Alireza Salemi, Sheshera Mysore, Michael Bendersky, and Hamed Zamani. 2024b · 2024
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Personalized Pieces: Efficient Personalized Large Language Models through Collaborative Efforts
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LLM-based Medical Assistant Personalization with Short- and Long-Term Memory Coordination. In Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers) , Kevin Duh, Helena Gomez, and Steven Bethard (Eds.). Association for Computational Linguistics, Mexico City, Mexico, 2386–2398
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LoRA Land: 310 Fine-tuned LLMs that Rival GPT-4, A Technical Report
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