d denotes the size of hidden states, pi denotes position embedding at position i, Aij denotes the attention score between a query and a key, ri−j denotes a learnable scalar based on the offset between the query and the key, and R...
PaLM(540B)使用了一个由社交媒体对话、过滤 后的网页、书籍、Github、多语言维基百科和新闻组成的预训 练数据集,共包含 7800 亿 token。 LLaMA 从多个数据源中提取训练数据,包括 CommonCrawl、C4 、Github、Wikipedia、书籍、ArXiv 和 StackExchange。LLaMA(6B)和 LLaMA(13B)的训练数 据大小为 1.0 万亿 token,而...
Fig. 7. A comparative illustration of in-context learning (ICL) and chain-of-thought (CoT) prompting. ICL prompts LLMs with a natural language description, several demonstrations, and a test query. While CoT prompting involves a series of intermediate reasoning steps in prompts. 提示公式化 如[...
原文地址:https://alphahinex.github.io/2023/05/21/a-survey-of-large-language-models/ description: "可作为了解当前大语言模型发展情况的材料进行阅读" date: 2023.05.21 10:34 categories: - Book tags: [Others] keywords: LLM, ICL, CoT, Transformer, RLHF ...
The organization of papers refers to our survey "A Survey of Large Language Models". Please let us know if you find out a mistake or have any suggestions by e-mail: batmanfly@gmail.com (we suggest ccing another email francis_kun_zhou@163.com meanwhile, in case of any unsuccessful deliv...
A Survey of Large Language Models Attribution [ArXiv preprint] 🌟 Introduction Open-domain dialogue systems, driven by large language models, have changed the way we use conversational AI. However, these systems often produce content that might not be reliable. In traditional open-domain settings...
这里的parameter efficient fine-tuning简称PEFT,大家看这个是不是觉得有一丝丝熟悉的感觉,Hugging face已经出了一个github.com/huggingface/的function了,Lora早已经收录其中。 相比于full parameter tuning,PEFT的方法是将预训练模型中绝大部分的参数都冻结 了,就是咱不碰大语言模型里的参数,而是通过比如prompt tuning...
A Survey of Large Language Models 大型语言模型综述,非常详细,格局打开!A Survey of Large Language Models 1.导读 讲得通俗易懂,且格局拉满!基本覆盖了自ChatGPT以来的AI比较火的事件,还多次提到强人工智能AGI(人工通用智能)。对近几年的大型语言模型( Large Language Models)进行了详细介绍。非常建议感兴趣大...
Large language models (LLMs) are gaining increasing popularity in both academia and industry, owing to their unprecedented performance in various applications. As LLMs continue to play a vital role in both research and daily use, their evaluation becomes increasingly critical, not only at the task...
去年6 月底,我们在 arXiv 上发布了业内首篇多模态大语言模型领域的综述《A Survey on Multimodal Large Language Models》,系统性梳理了多模态大语言模型的进展和发展方向,目前论文引用 120+,开源 GitHub 项目获得8.3K Stars。自论文发布以来,我们收到了很多读者非常宝贵的意见,感谢大家的支持!