Fetching the paper…
Reading the bibliography…
Recently, the personalization of Large Language Models (LLMs) to generate content that aligns with individual user preferences has garnered widespread attention.
Okapi at TREC-3
Stephen E Robertson, Steve Walker, Susan Jones, Micheline M Hancock-Beaulieu, Mike Gatford, et al · 1995
Earlier work this paper cites.
Rouge: A package for automatic evaluation of summaries. In Text summarization branches out . 74–81
Chin-Yew Lin. 2004 · 2004
Earlier work this paper cites.
Matrix factorization techniques for recommender systems
Yehuda Koren, Robert Bell, and Chris Volinsky. 2009 · 2009
Earlier work this paper cites.
Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba. 2014 · 2014
Earlier work this paper cites.
Neural collaborative filtering. In Proceedings of the 26th international conference on world wide web . 173–182
Xiangnan He, Lizi Liao, Hanwang Zhang, Liqiang Nie, Xia Hu, and Tat-Seng Chua. 2017 · 2017
Earlier work this paper cites.
Attention is all you need
A Vaswani. 2017 · 2017
Earlier work this paper cites.
Deep matrix factorization models for recommender systems.. In IJCAI , Vol. 17. Melbourne, Australia, 3203–3209
Hong-Jian Xue, Xinyu Dai, Jianbing Zhang, Shujian Huang, and Jiajun Chen. 2017 · 2017
Earlier work this paper cites.
Representation learning with contrastive predictive coding
Aaron van den Oord, Yazhe Li, and Oriol Vinyals. 2018 · 2018
Earlier work this paper cites.
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019 · 2019
Earlier work this paper cites.
Roberta: A robustly optimized bert pretraining approach
Yinhan Liu. 2019 · 2019
Earlier work this paper cites.
Neural graph collaborative filtering. In Proceedings of the 42nd international ACM SIGIR conference on Research and development in Information Retrieval . 165–174
Xiang Wang, Xiangnan He, Meng Wang, Fuli Feng, and Tat-Seng Chua. 2019 · 2019
Earlier work this paper cites.
Retrieval augmented language model pre-training. In International conference on machine learning . PMLR, 3929–3938
Kelvin Guu, Kenton Lee, Zora Tung, Panupong Pasupat, and Mingwei Chang. 2020 · 2020
Earlier work this paper cites.
Lightgcn: Simplifying and powering graph convolution network for recommendation. In Proceedings of the 43rd International ACM SIGIR conference on research and development in Information Retrieval . 639–648
Xiangnan He, Kuan Deng, Xiang Wang, Yan Li, Yongdong Zhang, and Meng Wang. 2020 · 2020
Earlier work this paper cites.
A survey on contrastive self-supervised learning
Ashish Jaiswal, Ashwin Ramesh Babu, Mohammad Zaki Zadeh, Debapriya Banerjee, and Fillia Makedon. 2020 · 2020
Earlier work this paper cites.
Retrieval-augmented generation for knowledge-intensive nlp tasks
Patrick Lewis, Ethan Perez, et al · 2020
Earlier work this paper cites.
Clear: Contrastive learning for sentence representation
Zhuofeng Wu, Sinong Wang, Jiatao Gu, Madian Khabsa, Fei Sun, and Hao Ma. 2020 · 2020
Earlier work this paper cites.
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 . 874–880
Gautier Izacard and Édouard Grave. 2021 · 2021
Earlier work this paper cites.
Improving language models by retrieving from trillions of tokens. In International conference on machine learning . PMLR, 2206–2240
Sebastian Borgeaud, Arthur Mensch, et al · 2022
Cited alongside, same era.
Few-shot learning with retrieval augmented language models
Gautier Izacard, Patrick Lewis, et al · 2022
Cited alongside, same era.
Uncovering chatgpt’s capabilities in recommender systems. In Proceedings of the 17th ACM Conference on Recommender Systems . 1126–1132
Sunhao Dai, Ninglu Shao, et al · 2023
Cited alongside, same era.
Retrieval-augmented generation for large language models: A survey
Yunfan Gao, Yun Xiong, Xinyu Gao, Kangxiang Jia, Jinliu Pan, Yuxi Bi, Yi Dai, Jiawei Sun, and Haofen Wang. 2023 · 2023
Cited alongside, same era.
Personalized soups: Personalized large language model alignment via post-hoc parameter merging
Pre-trained language models for text generation: A survey
Junyi Li, Tianyi Tang, Wayne Xin Zhao, Jian-Yun Nie, and Ji-Rong Wen. 2024b · 2024
Later among the works it cites.
Personalized language modeling from personalized human feedback
Xinyu Li, Zachary C Lipton, and Liu Leqi. 2024a · 2024
Later among the works it cites.
Optimization methods for personalizing large language models through retrieval augmentation. In Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval . 752–762
Alireza Salemi, Surya Kallumadi, and Hamed Zamani. 2024 · 2024
Later among the works it cites.
A survey of controllable learning: Methods and applications in information retrieval
Chenglei Shen, Xiao Zhang, Teng Shi, Changshuo Zhang, Guofu Xie, and Jun Xu. 2024 · 2024
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Joel Jang, Seungone Kim, et al · 2023
Cited alongside, same era.
Large language models struggle to learn long-tail knowledge. In International Conference on Machine Learning . PMLR, 15696–15707
Nikhil Kandpal, Haikang Deng, Adam Roberts, Eric Wallace, and Colin Raffel. 2023 · 2023
Cited alongside, same era.
Teach LLMs to Personalize–An Approach inspired by Writing Education
Cheng Li, Mingyang Zhang, Qiaozhu Mei, Yaqing Wang, Spurthi Amba Hombaiah, Yi Liang, and Michael Bendersky. 2023 · 2023
Cited alongside, same era.
Pearl: Personalizing large language model writing assistants with generation-calibrated retrievers
Sheshera Mysore, Zhuoran Lu, et al · 2023
Cited alongside, same era.
Integrating summarization and retrieval for enhanced personalization via large language models
Chris Richardson, Yao Zhang, Kellen Gillespie, Sudipta Kar, Arshdeep Singh, Zeynab Raeesy, Omar Zia Khan, and Abhinav Sethy. 2023 · 2023
Cited alongside, same era.
Lamp: When large language models meet personalization
Alireza Salemi, Sheshera Mysore, Michael Bendersky, and Hamed Zamani. 2023 · 2023
Cited alongside, same era.
C-Pack: Packaged Resources To Advance General Chinese Embedding
Shitao Xiao, Zheng Liu, Peitian Zhang, and Niklas Muennighoff. 2023 · 2023
Cited alongside, same era.
Siren’s song in the AI ocean: a survey on hallucination in large language models
Yue Zhang, Yafu Li, Leyang Cui, Deng Cai, Lemao Liu, Tingchen Fu, Xinting Huang, Enbo Zhao, Yu Zhang, Yulong Chen, et al · 2023
Cited alongside, same era.
REPLUG: Retrieval-Augmented Black-Box Language Models. In Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers) . 8364–8377
Weijia Shi, Sewon Min, Michihiro Yasunaga, Minjoon Seo, Richard James, Mike Lewis, Luke Zettlemoyer, and Wen-tau Yih. 2024a · 2024
Later among the works it cites.
Personalized Pieces: Efficient Personalized Large Language Models through Collaborative Efforts
Zhaoxuan Tan, Zheyuan Liu, and Meng Jiang. 2024a · 2024
Later among the works it cites.
A survey on large language models for recommendation
Likang Wu, Zhi Zheng, Zhaopeng Qiu, Hao Wang, Hongchao Gu, Tingjia Shen, Chuan Qin, Chen Zhu, Hengshu Zhu, Qi Liu, et al · 2024
Later among the works it cites.
FedLoRA: When Personalized Federated Learning Meets Low-Rank Adaptation
Xinghao Wu, Xuefeng Liu, Jianwei Niu, Haolin Wang, Shaojie Tang, and Guogang Zhu. 2024b · 2024
Later among the works it cites.
Fine-grained human feedback gives better rewards for language model training
Zeqiu Wu, Yushi Hu, Weijia Shi, Nouha Dziri, Alane Suhr, Prithviraj Ammanabrolu, Noah A Smith, Mari Ostendorf, and Hannaneh Hajishirzi. 2024a · 2024
Later among the works it cites.
An Yang, Baosong Yang, Binyuan Hui, Bo Zheng, Bowen Yu, Chang Zhou, Chengpeng Li, Chengyuan Li, Dayiheng Liu, Fei Huang, et al · 2024
Later among the works it cites.
Modeling Domain and Feedback Transitions for Cross-Domain Sequential Recommendation
Changshuo Zhang, Teng Shi, Xiao Zhang, Qi Liu, Ruobing Xie, Jun Xu, and Ji-Rong Wen. 2024c · 2024
Later among the works it cites.
QAGCF: Graph Collaborative Filtering for Q&A Recommendation
Changshuo Zhang, Teng Shi, Xiao Zhang, Yanping Zheng, Ruobing Xie, Qi Liu, Jun Xu, and Ji-Rong Wen. 2024d · 2024
Later among the works it cites.
Model-Agnostic Causal Embedding Learning for Counterfactually Group-Fair Recommendation
Xiao Zhang, Teng Shi, Jun Xu, Zhenhua Dong, and Ji-Rong Wen. 2024b · 2024
Later among the works it cites.
Dense text retrieval based on pretrained language models: A survey
Wayne Xin Zhao, Jing Liu, Ruiyang Ren, and Ji-Rong Wen. 2024 · 2024
Later among the works it cites.
HYDRA: Model Factorization Framework for Black-Box LLM Personalization
Yuchen Zhuang, Haotian Sun, Yue Yu, Qifan Wang, Chao Zhang, and Bo Dai. 2024 · 2024
Later among the works it cites.
Think Before Recommend: Unleashing the Latent Reasoning Power for Sequential Recommendation
Jiakai Tang, Sunhao Dai, Teng Shi, Jun Xu, Xu Chen, Wen Chen, Wu Jian, and Yuning Jiang. 2025 · 2025
Closest in time.
Test-Time Alignment for Tracking User Interest Shifts in Sequential Recommendation
Changshuo Zhang, Xiao Zhang, Teng Shi, Jun Xu, and Ji-Rong Wen. 2025 · 2025
Closest in time.