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Large language models (LLMs) like GPTs, trained on vast datasets, have demonstrated impressive capabilities in language understanding, reasoning, and planning, achieving human-level performance in various tasks.
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Latent tree models and approximate inference in bayesian networks
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Structured clinical interview for the dsm (scid)
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Retrieval induces adaptive forgetting of competing memories via cortical pattern suppression
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Deep reinforcement learning for mention-ranking coreference models
Kevin Clark and Christopher D. Manning · 2016
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Self-correcting models for model-based reinforcement learning
E. Talvitie · 2017
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Extracting low-dimensional dynamics from multiple large-scale neural population recordings by learning to predict correlations
Marcel Nonnenmacher, Srinivas C Turaga, and Jakob H Macke · 2017
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Deep reinforcement learning from human preferences
Paul F Christiano, Jan Leike, Tom Brown, Miljan Martic, Shane Legg, and Dario Amodei · 2017
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, and Lllia Polosukhin · 2017
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The fourth industrial revolution: Opportunities and challenges
Min Xu, Jeanne M. David, and Suk Hi Kim · 2018
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Icd-11 for mortality and morbidity statistics (2018)
World Health Organization et al · 2018
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Know what you don’t know: Unanswerable questions for squad
Pranav Rajpurkar, Robin Jia, and Percy Liang · 2018
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An approach to identify user preferences based on social network analysis
Stevan Milovanović, Zorica Bogdanović, Aleksandra Labus, Dušan Barać, and Marijana Despotović-Zrakić · 2019
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How much knowledge can you pack into the parameters of a language model?, 2020
Adam Roberts, Colin Raffel, and Noam Shazeer · 2020
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Economics for the future – beyond the superorganism
N.J. Hagens · 2020
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Language models are few-shot learners
Tom B. Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, Sandhini Agarwal, Ariel Herbert-Voss, Gretchen Krueger, Tom Henighan, Rewon Child, Aditya Ramesh, Daniel M. Ziegler, Jeffrey Wu, Clemens Winter, Christopher Hesse, Mark Chen, Eric Sigler, Mateusz Litwin, Scott Gray, Benjamin Chess, Jack Clark, Christopher Berner, Sam McCandlish, Alec Radford, Ilya Sutskever, and Dario Amodei · 2020
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In-depth behavior understanding and use: The behavior informatics approach
Longbing Cao · 2020
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Biometric authentication based on emg signals of speech
Muhammad Umar Khan, Zainoor Ahmad Choudry, Sumair Aziz, Syed Zohaib Hassan Naqvi, Afeefa Aymin, and Muhammad Atif Imtiaz · 2020
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Reuse, don’t retrain: A recipe for continued pretraining of language models
Jupinder Parmar, Sanjev Satheesh, Mostofa Patwary, Mohammad Shoeybi, and Bryan Catanzaro · 2020
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How does nlp benefit legal system: A summary of legal artificial intelligence
Haoxi Zhong, Chaojun Xiao, CunChao Tu, Tianyang Zhang, Zhiyuan Liu, and Maosong Sun · 2020
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The pile: An 800gb dataset of diverse text for language modeling
Leo Gao, Stella Biderman, Sid Black, Laurence Golding, Travis Hoppe, Charles Foster, Jason Phang, Horace He, Anish Thite, Noa Nabeshima, et al · 2020
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A thousand brains: A new theory of intelligence
Jeff Hawkins · 2021
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Frozen pretrained transformers as universal computation engines
Kevin Lu, Aditya Grover, Pieter Abbeel, and Igor Mordatch · 2022
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A reference data model for process-related user interaction logs
Luka Abb and Jana-Rebecca Rehse · 2022
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H-lps: a hybrid approach for user’s location privacy in location-based services
Sonia Sabir, Inayat Ali, and Eraj Khan · 2022
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D4: a chinese dialogue dataset for depression-diagnosis-oriented chat
Bing Yao, Changying Shi, Lei Zou, et al · 2022
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Murag: Multimodal retrieval-augmented generator for open question answering over images and text
Wenhu Chen, Hexiang Hu, Xi Chen, Pat Verga, and William W. Cohen · 2022
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Ra-cm3: Retrieval-augmented causal masked multimodal model
Michihiro Yasunaga, Armen Aghajanyan, Weijia Shi, Rich James, Jure Leskovec, Percy Liang, Mike Lewis, Luke Zettlemoyer, and Wen-tau Yih · 2022
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Re2g: Retrieve, rerank, generate, 2022
Michael Glass, Gaetano Rossiello, Md Faisal Mahbub Chowdhury, Ankita Rajaram Naik, Pengshan Cai, and Alfio Gliozzo · 2022
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Elle: Efficient lifelong pre-training for emerging data
Yujia Qin, Jiajie Zhang, Yankai Lin, Zhiyuan Liu, Peng Li, Maosong Sun, and Jie Zhou · 2022
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Towards continual knowledge learning of language models
Joel Jang, Seonghyeon Ye, Sohee Yang, Joongbo Shin, Janghoon Han, Gyeonghun Kim, Stanley Choi Jungkyu, and Minjoon Seo · 2022
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Training language models to follow instructions with human feedback
Long Ouyang, Jeff Wu, Xu Jiang, Diogo Almeida, Carroll L. Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, Alex Ray, John Schulman, Jacob Hilton, Fraser Kelton, Luke Miller, Maddie Simens, Amanda Askell, Peter Welinder, Paul Christiano, Jan Leike, and Ryan Lowe · 2022
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Train short, test long: Attention with linear biases enables input length extrapolation
Ofir Press, Noah A. Smith, and Mike Lewis · 2022
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Structured prompting: Scaling in-context learning to 1,000 examples
Yaru Hao, Yutao Sun, Li Dong, Zhixiong Han, Yuxian Gu, and Furu Wei · 2022
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Large language models encode clinical knowledge
Karan Singhal, Shekoofeh Azizi, Tao Tu, S. Sara Mahdavi, Jason Wei, Hyung Won Chung, Nathan Scales, Ajay Tanwani, Heather Cole-Lewis, Stephen Pfohl, Perry Payne, Martin Seneviratne, Paul Gamble, Chris Kelly, Nathaneal Scharli, Aakanksha Chowdhery, Philip Mansfield, Blaise Aguera y Arcas, Dale Webster, Greg S. Corrado, Yossi Matias, Katherine Chou, Juraj Gottweis, Nenad Tomasev, Yun Liu, Alvin Rajkomar, Joelle Barral, Christopher Semturs, Alan Karthikesalingam, and Vivek Natarajan · 2022
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Chain-of-thought prompting elicits reasoning in large language models
Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Fei Xia, Ed Chi, Quoc V Le, Denny Zhou, et al · 2022
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Llm-pruner: On the structural pruning of large language models
Xinyin Ma, Gongfan Fang, and Xinchao Wang · 2023
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Theory of mind for multi-agent collaboration via large language models
Huao Li, Yu Chong, Simon Stepputtis, Joseph Campbell, Dana Hughes, Charles Lewis, and Katia Sycara · 2023
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Are emergent abilities of large language models a mirage?
Rylan Schaeffer, Brando Miranda, and Sanmi Koyejo · 2023
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A review of cooperative multi-agent deep reinforcement learning
Ali Oroojlooy and Darius Adam Hajinezhad · 2023
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Data-centric ai: Perspectives and challenges
Daochen Zha, Zaid Pervaiz Bhat, Kwei-Herng Lai, Fan Yang, and Xia Hu · 2023
Training spiking neural networks using lessons from deep learning
Jason K. Eshraghian, Max Ward, Emre O. Neftci, Xinxin Wang, Gregor Lenz, Girish Dwivedi, Mohammed Bennamoun, Doo Seok Jeong, and Wei D. Lu · 2023
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Gaia: a benchmark for general ai assistants
Grégoire Mialon, Clémentine Fourrier, Craig Swift, Thomas Wolf, Yann LeCun, and Thomas Scialom · 2023
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User embedding model for personalized language prompting, 2024
Sumanth Doddapaneni, Krishna Sayana, Ambarish Jash, Sukhdeep Sodhi, and Dima Kuzmin · 2024
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A Survey on Self-Evolution of Large Language Models, June 2024
Zhengwei Tao, Ting-En Lin, Xiancai Chen, Hangyu Li, Yuchuan Wu, Yongbin Li, Zhi Jin, Fei Huang, Dacheng Tao, and Jingren Zhou · 2024
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Memory persistence: From fundamental mechanisms to translational opportunities
Susana A. Merlo, Mariano A. Belluscio, María E. Pedreira, et al · 2024
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alphaXiv searches the wider corpus for related work and actual follow-ups.
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Precision and personalized medicine: What their current definition says and silences about the model of health they promote. implication for the development of personalized health
C. Delpierre and T. Lefèvre · 2023
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The power of generative ai: A review of requirements, models, input-output formats, evaluation metrics, and challenges
Ajay Bandi, Pydi Venkata Satya Ramesh Adapa, and Yudu Eswar Vinay Pratap Kumar Kuchi · 2023
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Structure prediction in low dimensions: concepts, issues and examples
J. C. Schön · 2023
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Cognitive neuroscience perspective on memory: overview and summary
S Sridhar, A Khamaj, and MK Asthana · 2023
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Five-factor model personality traits and the trajectories of episodic memory: A meta-analysis of individual participant data from 120,640 participants and 471,821 memory assessments
Angelina R. Sutin, Justin Brown, Martina Luchetti, Damaris Aschwanden, Yannick Stephan, and Antonio Terracciano · 2023
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A thirst for knowledge: Grounding curiosity, creativity, and aesthetics in memory and reward neural systems
Y. N. Kenett, S. Humphries, and A. Chatterjee · 2023
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Memorybank: Enhancing large language models with long-term memory
Wanjun Zhong, Lin Guo, Qian Gao, Hao Ye, and Yijun Wang · 2024
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Perltqa: A personal long-term memory dataset for memory classification, retrieval, and fusion in question answering
Yiming Du, Hongru Wang, Zhengyi Zhao, Bin Liang, Baojun Wang, Wanjun Zhong, Zezhong Wang, and Kam-Fai Wong · 2024
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Trustworthy distributed ai systems: Robustness, privacy, and governance
W. Wei and L. Liu · 2024
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Unlocking the power of data: Effective data-driven marketing strategies to engage millennial consumers
Benediktus Rolando and Herry Mulyono · 2024
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Envgen: Generating and adapting environments via llms for training embodied agents
Abhay Zala, Jaemin Cho, Han Lin, Jaehong Yoon, and Mohit Bansal · 2024
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Y. Yu, Q. Zhang, J. Li, Q. Fu, and D. Ye · 2024
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Learning to (learn at test time): Rnns with expressive hidden states
Yu Sun, Xinhao Li, Karan Dalal, Jiarui Xu, Arjun Vikram, Genghan Zhang, Yann Dubois, Xinlei Chen, Xiaolong Wang, Sanmi Koyejo, Tatsunori Hashimoto, and Carlos Guestrin · 2024
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Raptor: Recursive abstractive processing for tree-organized retrieval
Parth Sarthi, Salman Abdullah, Aditi Tuli, Shubh Khanna, Anna Goldie, and Christopher D. Manning · 2024
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Memory³: Language modeling with explicit memory
H. Yang, Z. Lin, W. Wang, H. Wu, Z. Li, B. Tang, W. Wei, J. Wang, Z. Tang, S. Song, C. Xi, Y. Yu, K. Chen, F. Xiong, L. Tang, and W. E · 2024
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Compress to impress: Unleashing the potential of compressive memory in real-world long-term conversations, 2024
Nuo Chen, Hongguang Li, Juhua Huang, Baoyuan Wang, and Jia Li · 2024
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Blended rag: Improving rag accuracy with semantic search and hybrid query-based retrievers
Kunal Sawarkar, Abhilasha Mangal, and Shivam Raj Solanki · 2024
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Improving retrieval performance in rag pipelines with hybrid search
Leonie Monigatti · 2024
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Memocrs: Memory-enhanced sequential conversational recommender systems with large language models, 2024
Yunjia Xi, Weiwen Liu, Jianghao Lin, Bo Chen, Ruiming Tang, Weinan Zhang, and Yong Yu · 2024
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Albert Q. Jiang, Alexandre Sablayrolles, Antoine Roux, Arthur Mensch, Blanche Savary, Chris Bamford, Devendra Singh Chaplot, Diego de las Casas, Emma Bou Hanna, Florian Bressand, Gianna Lengyel, Guillaume Bour, Guillaume Lample, Lélio Renard Lavaud, Lucile Saulnier, Marie-Anne Lachaux, Pierre Stock, Sandeep Subramanian, Sophia Yang, Szymon Antoniak, Le Scao, Teven, Théophile Gervet, Thibaut Lavril, Thomas Wang, Timothée Lacroix, and El Sayed, William · 2024
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Saullm-7b: A pioneering large language model for law
Pierre Colombo, Telmo Pessoa Pires, Malik Boudiaf, Dominic Culver, Rui Melo, Caio Corro, Andre F. T. Martins, Fabrizio Esposito, Vera Lucia Raposo, Sofia Morgado, and Desa Michael · 2024
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Simple and scalable strategies to continually pre-train large language models
Adam Ibrahim, Benjamin Therien, Kshitij Gupta, Mats L. Richter, Quentin Anthony, Timonthee Lesort, Eugene Belilovsky, and Irina Rish · 2024
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Llama pro: Progressive llama with block expansion
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Large language model instruction following: A survey of progresses and challenges
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Instruction tuning with loss over instructions
Zhengyan shi, Adam X. Yang, Bin Wu, Laurence Aitchison, Emine YilmaZ, and Aldo Lipani · 2024
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Hello again! llm-powered personalized agent for long-term dialogue
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A comprehensive survey of llm alignment techniques: Rlhf, rlaif, ppo, dpo and more
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Direct preference optimization: Your language model is secretly a reward model
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Human-aware loss functions (halos)
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Orpo: Monolithic preference optimization without reference model
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