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Personalization has emerged as a critical research area in modern intelligent systems, focusing on mining users' behavioral history and adapting to their preferences for delivering tailored experiences.
The probabilistic relevance framework: Bm25 and beyond
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Improving language understanding by generative pre-training
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BERT: Pre-training of deep bidirectional transformers for language understanding
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Parameter-efficient transfer learning for nlp
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Language models are unsupervised multitask learners
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Fine-tuning language models from human preferences
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Language models are few-shot learners
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Lora: Low-rank adaptation of large language models
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Unsupervised dense information retrieval with contrastive learning
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Finetuned language models are zero-shot learners
J. Wei, M. Bosma, V. Zhao, K. Guu, A. W. Yu, B. Lester, N. Du, A. M. Dai, and Q. V. Le · 2021
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Palm: Scaling language modeling with pathways
A. Chowdhery, S. Narang, J. Devlin, M. Bosma, G. Mishra, A. Roberts, P. Barham, H. W. Chung, C. Sutton, S. Gehrmann, et al · 2022
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Debertav3: Improving deberta using electra-style pre-training with gradient-disentangled embedding sharing
P. He, J. Gao, and W. Chen · 2022
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P-tuning: Prompt tuning can be comparable to fine-tuning across scales and tasks
X. Liu, K. Ji, Y. Fu, W. Tam, Z. Du, Z. Yang, and J. Tang · 2022
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Introducing chatgpt
OpenAI · 2022
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Training language models to follow instructions with human feedback
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Black-box tuning for language-model-as-a-service
T. Sun, Y. Shao, H. Qian, X. Huang, and X. Qiu · 2022
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Open problems and fundamental limitations of reinforcement learning from human feedback
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Polyie: A dataset of information extraction from polymer material scientific literature
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Large language models for user interest journeys
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Uncovering chatgpt’s capabilities in recommender systems
S. Dai, N. Shao, H. Zhao, W. Yu, Z. Si, C. Xu, Z. Sun, X. Zhang, and J. Xu · 2023
Cited alongside, same era.
Retrieval-augmented recommender system: Enhancing recommender systems with large language models
D. Di Palma · 2023
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Towards next-generation intelligent assistants leveraging llm techniques
X. L. Dong, S. Moon, Y. E. Xu, K. Malik, and Z. Yu · 2023
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k k nn-adapter: Efficient domain adaptation for black-box language models
Y. Huang, D. Liu, Z. Zhong, W. Shi, and Y. T. Lee · 2023
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Personalized soups: Personalized large language model alignment via post-hoc parameter merging
J. Jang, S. Kim, B. Y. Lin, Y. Wang, J. Hessel, L. Zettlemoyer, H. Hajishirzi, Y. Choi, and P. Ammanabrolu · 2023
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Toolchain*: Efficient action space navigation in large language models with a* search
Y. Zhuang, X. Chen, T. Yu, S. Mitra, V. Bursztyn, R. A. Rossi, S. Sarkhel, and C. Zhang · 2023
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Large language models are zero-shot rankers for recommender systems
Y. Hou, J. Zhang, Z. Lin, H. Lu, R. Xie, J. McAuley, and W. X. Zhao · 2024
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Personalized language modeling from personalized human feedback, 2024
X. Li, Z. C. Lipton, and L. Leqi · 2024
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Personal llm agents: Insights and survey about the capability, efficiency and security
Y. Li, H. Wen, W. Wang, X. Li, Y. Yuan, G. Liu, J. Liu, W. Xu, X. Wang, Y. Sun, et al · 2024
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Tuning language models by proxy, 2024
A. Liu, X. Han, Y. Wang, Y. Tsvetkov, Y. Choi, and N. A. Smith · 2024
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Do llms understand user preferences? evaluating llms on user rating prediction, 2023
W.-C. Kang, J. Ni, N. Mehta, M. Sathiamoorthy, L. Hong, E. Chi, and D. Z. Cheng · 2023
Cited alongside, same era.
H. R. Kirk, B. Vidgen, P. Röttger, and S. A. Hale · 2023
Cited alongside, same era.
Teach llms to personalize–an approach inspired by writing education
C. Li, M. Zhang, Q. Mei, Y. Wang, S. A. Hombaiah, Y. Liang, and M. Bendersky · 2023
Cited alongside, same era.
Is chatgpt a good recommender? a preliminary study
J. Liu, C. Liu, P. Zhou, R. Lv, K. Zhou, and Y. Zhang · 2023
Cited alongside, same era.
Pearl: Personalizing large language model writing assistants with generation-calibrated retrievers
S. Mysore, Z. Lu, M. Wan, L. Yang, S. Menezes, T. Baghaee, E. B. Gonzalez, J. Neville, and T. Safavi · 2023
Cited alongside, same era.
Gpt-4 technical report
OpenAI · 2023
Cited alongside, same era.
CombLM: Adapting black-box language models through small fine-tuned models
A. Ormazabal, M. Artetxe, and E. Agirre · 2023
Cited alongside, same era.
Once: Boosting content-based recommendation with both open-and closed-source large language models
Q. Liu, N. Chen, T. Sakai, and X.-M. Wu · 2024
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Llm-rec: Personalized recommendation via prompting large language models
H. Lyu, S. Jiang, H. Zeng, Y. Xia, Q. Wang, S. Zhang, R. Chen, C. Leung, J. Tang, and J. Luo · 2024
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Direct preference optimization: Your language model is secretly a reward model
R. Rafailov, A. Sharma, E. Mitchell, C. D. Manning, S. Ermon, and C. Finn · 2024
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Optimization methods for personalizing large language models through retrieval augmentation
A. Salemi, S. Kallumadi, and H. Zamani · 2024
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Medadapter: Efficient test-time adaptation of large language models towards medical reasoning
W. Shi, R. Xu, Y. Zhuang, Y. Yu, H. Wu, C. Yang, and M. D. Wang · 2024
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Ehragent: Code empowers large language models for few-shot complex tabular reasoning on electronic health records
W. Shi, R. Xu, Y. Zhuang, Y. Yu, J. Zhang, H. Wu, Y. Zhu, J. C. Ho, C. Yang, and M. D. Wang · 2024
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Adaplanner: Adaptive planning from feedback with language models
H. Sun, Y. Zhuang, L. Kong, B. Dai, and C. Zhang · 2024
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Bbox-adapter: Lightweight adapting for black-box large language models, 2024
H. Sun, Y. Zhuang, W. Wei, C. Zhang, and B. Dai · 2024
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Democratizing large language models via personalized parameter-efficient fine-tuning
Z. Tan, Q. Zeng, Y. Tian, Z. Liu, B. Yin, and M. Jiang · 2024
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FedloRA: When personalized federated learning meets low-rank adaptation, 2024
X. Wu, X. Liu, J. Niu, H. Wang, S. Tang, and G. Zhu · 2024
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Fine-grained human feedback gives better rewards for language model training
Z. Wu, Y. Hu, W. Shi, N. Dziri, A. Suhr, P. Ammanabrolu, N. A. Smith, M. Ostendorf, and H. Hajishirzi · 2024
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Ram-ehr: Retrieval augmentation meets clinical predictions on electronic health records
R. Xu, W. Shi, Y. Yu, Y. Zhuang, B. Jin, M. D. Wang, J. C. Ho, and C. Yang · 2024
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Bmretriever: Tuning large language models as better biomedical text retrievers
R. Xu, W. Shi, Y. Yu, Y. Zhuang, Y. Zhu, M. D. Wang, J. C. Ho, C. Zhang, and C. Yang · 2024
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A survey on large language model (llm) security and privacy: The good, the bad, and the ugly
Y. Yao, J. Duan, K. Xu, Y. Cai, Z. Sun, and Y. Zhang · 2024
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Recommender systems in the era of large language models (llms)
Z. Zhao, W. Fan, J. Li, Y. Liu, X. Mei, Y. Wang, Z. Wen, F. Wang, X. Zhao, J. Tang, et al · 2024
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Judging llm-as-a-judge with mt-bench and chatbot arena
L. Zheng, W.-L. Chiang, Y. Sheng, S. Zhuang, Z. Wu, Y. Zhuang, Z. Lin, Z. Li, D. Li, E. Xing, et al · 2024
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Toolqa: A dataset for llm question answering with external tools
Y. Zhuang, Y. Yu, K. Wang, H. Sun, and C. Zhang · 2024
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