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  • arXiv:2310. 07343v1 [cs. CL] 11 Oct 2023
    Large language models (LLMs) (Brown et al , 2020;Ouyang et al ,2022;Chowdhery et al , 2022;Zhang et al ,2022;OpenAI,2023b;Tou-vron et al ,2023;Anil et al ,2023) trained on mas-sive corpora from various sources (e g , Wikipedia, Books, Github) implicitly store enormous amounts of world knowledge in their parameters (Petroni
  • List of large language models - Wikipedia
    A large language model (LLM) is a type of machine learning model designed for natural language processing tasks such as language generation LLMs are language models with many parameters, and are trained with self-supervised learning on a vast amount of text
  • zhangxjohn LLM-Agent-Benchmark-List - GitHub
    [2023 11] M3Exam: A Multilingual, Multimodal, Multilevel Benchmark for Examining Large Language Models Wenxuan Zhang (Alibaba) et al arXiv [ paper ] [ project page ]
  • (PDF) LLaMA: Open and Efficient Foundation Language Models - ResearchGate
    We introduce LLaMA, a collection of foundation language models ranging from 7B to 65B parameters We train our models on trillions of tokens, and show that it is possible to train
  • LLaMA: Open and Efficient Foundation Language Models
    We introduce LLaMA, a collection of foundation language models ranging from 7B to 65B parameters We train our models on trillions of tokens, and show that it is possible to train state-of-the-art models using publicly available datasets exclusively, without resorting to proprietary and inaccessible datasets
  • [2310. 07343] How Do Large Language Models Capture the Ever-changing . . .
    View a PDF of the paper titled How Do Large Language Models Capture the Ever-changing World Knowledge? A Review of Recent Advances, by Zihan Zhang and 4 other authors Although large language models (LLMs) are impressive in solving various tasks, they can quickly be outdated after deployment
  • Paper page - Skywork: A More Open Bilingual Foundation Model - Hugging Face
    In this technical report, we present Skywork-13B, a family of large language models (LLMs) trained on a corpus of over 3 2 trillion tokens drawn from both English and Chinese texts This bilingual foundation model is the most extensively trained and openly published LLMs of comparable size to date
  • Topics, Authors, and Institutions in Large Language Model Research . . .
    Large language models (LLMs) are dramatically influencing AI research, spurring discussions on what has changed so far and how to shape the field’s future To clarify such questions, we analyze a new dataset of 16,979 LLM-related arXiv papers, focusing on recent trends in 2023 vs 2018-2022
  • Agents with foundation models: advance and vision
    On the opportunities and risks of foundation models 2021, arXiv preprint arXiv: 2108 07258 Xi Z, Chen W, Guo X, He W, Ding Y, et al The rise and potential of large language model based agents: a survey 2023, arXiv preprint arXiv: 2309 07864 Wang L, Ma C, Feng X, Zhang Z, Yang H, Zhang J, Chen Z, Tang J, Chen X, Lin Y, Zhao W X, Wei Z, Wen J
  • qingsongedu Awesome-TimeSeries-SpatioTemporal-LM-LLM
    A professionally curated list of Large (Language) Models and Foundation Models (LLM, LM, FM) for Temporal Data (Time Series, Spatio-temporal, and Event Data) with awesome resources (paper, code, data, etc ), which aims to comprehensively and systematically summarize the recent advances to the best of our knowledge





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