微调大模型(Finetuning Large Language Models)—Data_preparation(四),本节讲述了大模型微调前的数据准备工作,最重要的是模型的tokenizer以及截断策略和数据的划分,自己的数据集在制作过程中,仅
Large language models (LLMs) have shown superior performance in various areas. And LLMs have the potential to revolutionize data management by serving as t
Large language models (LLMs) are advanced AI systems designed to understand human language intricacies and generate intelligent, creative responses to queries. Successful LLM are trained on enormous data sets typically measured in petabytes. This training data is sourced from books, articles, websites...
Large Language Models for Data Annotation: A Survey Zhen Tan, Dawei Li, Alimohammad Beigi, Song Wang, Ruocheng Guo, Amrita Bhattacharjee, Bohan Jiang, Mansooreh Karami, Jundong Li, Lu Cheng, Huan Liu 2024 Can LLMs Learn from Previous Mistakes? Investigating LLMs' Errors to Boost for Reason...
(5)Data Extraction and Parsing:从原始数据中提取有用成分,可能涉及HTML解析、PDF文本提取等。 (6)Encoding Detection:使用编码检测工具来识别文本编码,确保文本以正确的编码格式存储。 (7)Language Detection:利用语言检测工具来识别文本的语言,从而能够根据不同的语言将数据分割成子集,然后选择所需的语言文本。
Large Language Models as Data Augmenters for Cold-Start Item Recommendation论文阅读笔记 Abstract LLM的推理和泛化能力可以帮助我们更好地理解用户的偏好和项目特征。我们建议利用LLM作为数据增强器,来弥补在训练过程中对冷启动项目的知识差距。我们使用LLM根据用户历史行为的文本描述和新项目描述来推断用户对冷...
例如,在人工智能领域中,可能会收集大量的指令(如用户的查询、请求等),并为这些指令提供相应的回答或解决方案,从而构建出instruction-following data。这种数据对于训练和改进人工智能模型(如聊天机器人、问答系统等)非常重要,因为它们可以帮助模型理解并遵循用户的指令,从而提供更准确、有用的响应。 与Alpaca和Vicuna一样...
large language model data augmentation Large language model data augmentation refers to the process of increasing the amount and diversity of data used to train large language models (LLMs). This is an important technique as it can help improve the performance and generalization ability of the ...
大型语言模型（Large Language Models，LLM）大型语言模型（Large Language Models，LLM）是人工智能领域中的一种技术，它们通常由数亿甚至数十亿个参数构成，能够处理和生成自然语言文本。这些模型通过在大量文本数据上进行训练，学习语言的模式和结构，从而能够执行多种语言任务，如文本生成、翻译、摘要、问答等。一、大型...
However, current general Large Language Models (LLMs) cannot satisfy the strict requirements to correctness of generative texts in specific tasks of medical record generation. In addition, due to the constraints to protect patient privacy, physicians cannot upload patient data to public cloud services...