HiGPT: Heterogeneous Graph Language Model
专题命中 预训练与数据 :language model(title);分类 cs.CL、cs.LG
Comments Accepted by KDD'2024, full paper
AI 大模型
大语言模型、预训练、指令微调、后训练和语言模型应用。
专题命中 预训练与数据 :language model(title);分类 cs.CL、cs.LG
Comments Accepted by KDD'2024, full paper
专题命中 预训练与数据 :language model(title);分类 cs.CL、cs.AI
专题命中 预训练与数据 :large language model(abstract,comments);language model(abstract,comments);分类 cs.CL、cs.AI
Comments Natural language processing, large language models, generative AI, student evaluations of teaching, codebook generation, qualitative data analysis
专题命中 预训练与数据 :language model(title);分类 cs.CL、cs.AI
Comments Simulation of Conversational Intelligence in Chat, EACL 2024
专题命中 预训练与数据 :pretraining(title);分类 cs.AI、cs.LG
专题命中 预训练与数据 :pretraining(title);分类 cs.AI、cs.LG
专题命中 预训练与数据 :pretraining(title);分类 cs.AI、cs.LG
专题命中 预训练与数据 :prompting(title);分类 cs.AI、cs.LG
专题命中 预训练与数据 :pretraining(title);分类 cs.CL、cs.AI
Comments [TL;DR] we design and release the SNARE, the first large-scale multimodal alignment probing benchmark for current vision-language pretrained models
专题命中 预训练与数据 :language model(title);分类 cs.CL、cs.LG
Comments published in 16th UYMS (2023) https://ekitap.atauni.edu.tr/index.php/product/16-ulusal-yazilim-muhendisligi-sempozyumu-bildiri-kitabi/
Journal ref Ulusal Yazılım Mühendisliği Sempozyumu, 16, 155-165 (Erzurum 2023)
专题命中 预训练与数据 :language model(title);分类 cs.CL、cs.LG
Comments Published at ICML 2023. Blog post available at https://cs.stanford.edu/~myasu/blog/racm3/
专题命中 预训练与数据 :pretraining(title);分类 cs.CL、cs.LG
Comments Accepted to Interspeech 2023
专题命中 预训练与数据 :pretraining(title);分类 cs.AI、cs.LG
Comments CVPR 2023. Code/models: https://github.com/facebookresearch/omnivore
专题命中 预训练与数据 :language model(title);分类 cs.AI、cs.LG
Comments Accepted to CVPR 2023
专题命中 预训练与数据 :language model(title);分类 cs.CL、cs.AI
专题命中 预训练与数据 :pretraining(title);分类 cs.CL、cs.AI
Comments NeurIPS 2022
专题命中 预训练与数据 :pretraining(title);分类 cs.AI、cs.LG
Comments Extended Abstract presented at Machine Learning for Health (ML4H) symposium 2022, November 28th, 2022, New Orleans, United States & Virtual, http://www.ml4h.cc, 9 pages
专题命中 预训练与数据 :pretraining(title);分类 cs.AI、cs.LG
Comments Thirty-sixth Conference on Neural Information Processing Systems (NeurIPS 2022)
专题命中 预训练与数据 :pretraining(title);分类 cs.AI、cs.LG
Comments Accepted at ICML 2022 Workshop on Pre-training: Perspectives, Pitfalls, and Paths Forward, source code is available at https://github.com/facebookresearch/ppuda
专题命中 预训练与数据 :pretraining(title);分类 cs.CL、cs.AI
Comments *SEM 2022
专题命中 预训练与数据 :pretraining(title);分类 cs.CL、cs.LG
专题命中 预训练与数据 :language model(title);分类 cs.CL、cs.LG
专题命中 预训练与数据 :language model(title);分类 cs.CL、cs.AI
Comments 12 pages, 4 figures
专题命中 预训练与数据 :language model(title);分类 cs.CL、cs.AI
专题命中 预训练与数据 :pretraining(title);分类 cs.CL、cs.AI
Journal ref In Proceedings of the 3rd Workshop on Research in Computational Typology and Multilingual NLP of the 20th Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technologies in 2021
专题命中 预训练与数据 :language model(title);分类 cs.CL、cs.AI
Comments NAACL 2021
专题命中 预训练与数据 :pretraining(title);分类 cs.CL、cs.LG
Comments 7 pages, 1 figure, to be published in 25th International Conference on Pattern Recognition, ICPR 2020
专题命中 预训练与数据 :pretraining(title);分类 cs.AI、cs.LG
Comments 5 pages, 2019 19th International Conference on Control, Automation and Systems (ICCAS 2019)
专题命中 预训练与数据 :language model(title);分类 cs.CL、cs.AI
Comments 17 pages, 20 figures and/or tables
叠加产生稳健的神经扩展
机构 * Massachusetts Institute of Technology(麻省理工学院)
专题命中 预训练与数据 :large language model(abstract);language model(abstract);分类 cs.CL、cs.AI、cs.LG
AI总结 研究发现表示叠加是神经扩展定律的核心驱动因素,揭示了损失与模型规模之间的反比关系。
Comments Best Paper Runner-up at NeurIPS 2025
Journal ref Advances in Neural Information Processing Systems 38 (2025) 159269--159305