![]() In International Conference on Learning Representations. ExT5: Towards Extreme Multi-Task Scaling for Transfer Learning. Tran, Dara Bahri, Jianmo Ni, Jai Gupta, Kai Hui, Sebastian Ruder, and Donald Metzler. Vamsi Aribandi, Yi Tay, Tal Schuster, Jinfeng Rao, Huaixiu Steven Zheng, Sanket Vaibhav Mehta, Honglei Zhuang, Vinh Q.To help advance future research on Recommendation as Language Processing (RLP), Personalized Foundation Models (PFM), and Universal Recommendation Engine (URE), we release the source code, dataset, prompts, and pretrained P5 model at. On several benchmarks, we conduct experiments to show the effectiveness of P5. With adaptive personalized prompt for different users, P5 is able to make predictions in a zero-shot or few-shot manner and largely reduces the necessity for extensive fine-tuning. P5 advances recommender systems from shallow model to deep model to big model, and will revolutionize the technical form of recommender systems towards universal recommendation engine. Thus, it serves as the foundation model for various downstream recommendation tasks, allows easy integration with other modalities, and enables instruction-based recommendation. Specifically, P5 learns different tasks with the same language modeling objective during pretraining. ![]() The rich information from natural language assists P5 to capture deeper semantics for personalization and recommendation. In P5, all data such as user-item interactions, user descriptions, item metadata, and user reviews are converted to a common format - natural language sequences. To deal with such issues, considering that language can describe almost anything and language grounding is a powerful medium to represent various problems or tasks, we present a flexible and unified text-to-text paradigm called “Pretrain, Personalized Prompt, and Predict Paradigm” (P5) for recommendation, which unifies various recommendation tasks in a shared framework. As a result, it is hard to transfer the knowledge and representations from one task to another, thus restricting the generalization ability of existing recommendation approaches. ![]() In your spare time you can … Never mind.For a long time, different recommendation tasks require designing task-specific architectures and training objectives. You will also be teaching approximately 450 undergraduates how to identify and represent linguistic structure. (We’ll be fine as long as you don’t all stand on the porch at the same time.) As a group you’ll be working on 6 dissertations and 10 qualifying papers. We have 25 students currently in the program. I found some verbs: greet, hail, appreciate, and some adjectives: desirable, promising, refreshing and, yes, accepted. While struggling with this note I looked up synonyms for “welcome”. Alphabetically by first name they are: Augustina, Deepak, Livia, Morgan, and Nicholas. ![]() Five new students are joining us this fall. On behalf of the graduate faculty, I’m delighted to welcome everyone to another year jam-packed with classes, events, crises, programs, traffic jams, and celebrations. We are all looking forward to another productive and stimulating academic year. If you are a visiting scholar, the next person to consult is probably your faculty sponsor. If you are a new graduate student, the next person to consult is the graduate program director, Prof. If you are a new faculty member, the next person to consult is the department chair (that would be me). administrator, Danielle Berlingieri, is usually the best person to ask first. For basic administrative matters, our dept. If you have any questions or difficulties, please feel free to ask for assistance. As the *new* chair of the Linguistics Department, I would like to welcome you to Rutgers University, and to the Linguistics Department! I hope that all of you are getting settled here in central New Jersey.
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