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User modeling (UM) aims to discover patterns or learn representations from user data about the characteristics of a specific user, such as profile, preference, and personality.
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Twitter-based user modeling for news recommendations
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E-commerce recommendation with personalized promotion
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node2vec: Scalable feature learning for networks
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metapath2vec: Scalable representation learning for heterogeneous networks
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A user profile modeling method based on word2vec
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Attention is all you need
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User profiling approaches, modeling, and personalization
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Graph attention networks
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Graph convolutional neural networks for web-scale recommender systems
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Semi-supervised user profiling with heterogeneous graph attention networks
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Graph neural networks for social recommendation
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Language models are unsupervised multitask learners
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Bert4rec: Sequential recommendation with bidirectional encoder representations from transformer
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Language models are few-shot learners
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Lightgcn: Simplifying and powering graph convolution network for recommendation
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Heterogeneous graph transformer
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Pre-trained models for natural language processing: A survey
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Exploring the limits of transfer learning with a unified text-to-text transformer
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Calendar graph neural networks for modeling time structures in spatiotemporal user behaviors
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Identifying referential intention with heterogeneous contexts
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Detecting hate speech with gpt-3
K.-L. Chiu, A. Collins, and R. Alexander · 2021
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Lora: Low-rank adaptation of large language models
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The power of scale for parameter-efficient prompt tuning
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The inductive bias of in-context learning: Rethinking pretraining example design
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A survey on representation learning for user modeling
S. Li and H. Zhao · 2021
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Prefix-tuning: Optimizing continuous prompts for generation
X. L. Li and P. Liang · 2021
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A token-level reference-free hallucination detection benchmark for free-form text generation
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Survey of generative methods for social media analysis
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Human-compatible artificial intelligence
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Modeling co-evolution of attributed and structural information in graph sequence
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Finetuned language models are zero-shot learners
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Relation-aware heterogeneous graph for user profiling
Q. Yan, Y. Zhang, Q. Liu, S. Wu, and L. Wang · 2021
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Enabling classifiers to make judgements explicitly aligned with human values
Y. Bang, T. Yu, A. Madotto, Z. Lin, M. Diab, and P. Fung · 2022
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Towards automated generation and evaluation of questions in educational domains
S. Bhat, H. A. Nguyen, S. Moore, J. Stamper, M. Sakr, and E. Nyberg · 2022
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Language models are realistic tabular data generators
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A personalized dialogue generator with implicit user persona detection
I. Cho, D. Wang, R. Takahashi, and H. Saito · 2022
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Palm: Scaling language modeling with pathways
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Scaling instruction-finetuned language models
H. W. Chung, L. Hou, S. Longpre, B. Zoph, Y. Tay, W. Fedus, E. Li, X. Wang, M. Dehghani, S. Brahma, et al · 2022
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Are large pre-trained language models leaking your personal information?
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X. Liu, Z. Zhang, Y. Wang, Y. Lan, and C. Shen · 2022
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Training language models to follow instructions with human feedback
L. Ouyang, J. Wu, X. Jiang, D. Almeida, C. Wainwright, P. Mishkin, C. Zhang, S. Agarwal, K. Slama, A. Ray, et al · 2022
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Leveraging large language models for multiple choice question answering
J. Robinson, C. M. Rytting, and D. Wingate · 2022
Psy-llm: Scaling up global mental health psychological services with ai-based large language models
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alphaXiv searches the wider corpus for related work and actual follow-ups.
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Bloom: A 176b-parameter open-access multilingual language model
T. L. Scao, A. Fan, C. Akiki, E. Pavlick, S. Ilić, D. Hesslow, R. Castagné, A. S. Luccioni, F. Yvon, M. Gallé, et al · 2022
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W. Yu, C. Zhu, Z. Li, Z. Hu, Q. Wang, H. Ji, and M. Jiang · 2022
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Glm-130b: An open bilingual pre-trained model
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Least-to-most prompting enables complex reasoning in large language models
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