This paper embarks on an exploration into the Large Language Model (LLM)
datasets, which play a crucial role in the remarkable advancements of LLMs. The
datasets serve as the foundational infrastructure analogous to a root system
that sustains and nurtures the development of LLMs. Consequently, examination
of these datasets emerges as a critical topic in research. In order to address
the current lack of a comprehensive overview and thorough analysis of LLM
datasets, and to gain insights into their current status and future trends,
this survey consolidates and categorizes the fundamental aspects of LLM
datasets from five perspectives: (1) Pre-training Corpora; (2) Instruction
Fine-tuning Datasets; (3) Preference Datasets; (4) Evaluation Datasets; (5)
Traditional Natural Language Processing (NLP) Datasets. The survey sheds light
on the prevailing challenges and points out potential avenues for future
investigation. Additionally, a comprehensive review of the existing available
dataset resources is also provided, including statistics from 444 datasets,
covering 8 language categories and spanning 32 domains. Information from 20
dimensions is incorporated into the dataset statistics. The total data size
surveyed surpasses 774.5 TB for pre-training corpora and 700M instances for
other datasets. We aim to present the entire landscape of LLM text datasets,
serving as a comprehensive reference for researchers in this field and
contributing to future studies. Related resources are available at:
this https URL

该研究论文对大型语言模型数据集进行了综述和分类，包括预训练语料库、微调数据集、偏好数据集、评估数据集和传统自然语言处理数据集等五个方面；此外还提供了现有数据集资源的综合评估，涵盖 8 个语言类别和 32 个领域，包括来自 444 个数据集的统计信息，共计超过 774.5 TB 的预训练语料库数据和 7 亿个实例的其他数据集数据；旨在为研究人员提供整个 LLM 文本数据集的全貌，并为未来的研究做出贡献。

大型语言模型的数据集：综述

Datasets for Large Language Models: A Comprehensive Survey

Generative Adversarial Nets (GAN) have received considerable attention since
the 2014 groundbreaking work by Goodfellow et al. Such attention has led to an
explosion in new ideas, techniques and applications of GANs. To better
understand GANs we need to understand the mathematical foundation behind them.
This paper attempts to provide an overview of GANs from a mathematical point of
view. Many students in mathematics may find the papers on GANs more difficulty
to fully understand because most of them are written from computer science and
engineer point of view. The aim of this paper is to give more mathematically
oriented students an introduction to GANs in a language that is more familiar
to them.

本文从数学角度提供了生成式对抗网络（GAN）的概述，以更习惯数学语言的学生为目标，介绍了 GAN 的数学基础、应用及技术发展。

生成对抗网络（GAN）的数学介绍

A Mathematical Introduction to Generative Adversarial Nets (GAN)

This invited chapter in the Handbook of Quantum Logic and Quantum Structures
consists of two parts: 1. A substantially updated version of quant-ph/0402130
by the same authors, which initiated the area of categorical quantum mechanics,
but had not yet been published in full length; 2. An overview of the progress
which has been made since then in this area.

本文主要包括两部分内容，第一部分是 AB 两位作者的 quant-ph/0402130 论文的更新版本，该论文开创了范畴量子力学领域，但是尚未完整出版；第二部分则概述了这一领域自那时以来所取得的进展情况。