LLMs：大型语言模型进化树结构图之模型(BERT-style/GPT-style)、数据(预训练数据/微调数据/测试数据)、NLP任务(五大任务+效率+可信度+基准指令调优+对齐)、三大类模型的使用

LLMs：大型语言模型进化树结构图之模型(BERT-style/GPT-style)、数据(预训练数据/微调数据/测试数据)、NLP任务(五大任务+效率+可信度+基准指令调优+对齐)、三大类模型的使用和限制(Encoder-only、Encoder-Decoder、Decoder-only)

目录

大型语言模型进化树结构图之模型(BERT-style/GPT-style)、数据(预训练数据/微调数据/测试数据)、NLP任务(五大任务+效率+可信度+基准指令调优+对齐)

大型语言模型进化树结构图

一、模型的实用指南：BERT-style/GPT-style

1.1、BERT-style 的语言模型：编码器-解码器或仅编码器

1.2、GPT-style的语言模型：仅解码器

二、数据实用指南：预训练数据/微调数据/测试数据

2.1、预训练数据

2.2、微调数据

2.3、测试数据/用户数据

三、自然语言处理任务的实用指南：NLP五大任务+效率+可信度+基准指令调优+对齐

3.1、传统的NLU任务

3.2、生成任务

3.3、知识密集型任务

3.4、随着规模增长的能力

3.5、特定任务

3.6、现实世界的任务

3.7、效率Efficiency

3.7.1、成本Cost

3.7.2、延迟Latency

3.7.3、参数高效微调Parameter-Efficient Fine-Tuning

3.7.4、预训练系统Pretraining System

3.8、可信度Trustworthiness

3.8.1、鲁棒性和校准性Robustness and Calibration

3.8.2、虚假偏见Spurious biases

3.8.3、安全问题Safety issues

3.9、基准指令调优Benchmark Instruction Tuning

3.10、对齐Alignment

3.10.1、安全对齐（无害）Safety Alignment (Harmless)

3.10.2、真实对齐（诚实） Truthfulness Alignment (Honest)

3.10.3、指导实践（有帮助）Practical Guides for Prompting (Helpful)

3.10.4、开源社区的对齐努力Alignment Efforts of Open-source Communtity

四、三大类模型的使用和限制：Encoder-only、Encoder-Decoder、Decoder-only

4.1、Encoder-only

4.2、Encoder-Decoder

4.3、Decoder-only

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大型语言模型进化树结构图之模型(BERT-style/GPT-style)、数据(预训练数据/微调数据/测试数据)、NLP任务(五大任务+效率+可信度+基准指令调优+对齐)

大型语言模型进化树结构图

        该列表基于我们的调研论文《在实践中利用LLMs的威力：关于ChatGPT及其后续发展的调研》，以及@xinyadu的努力。该调研部分基于这篇博客的后半部分。我们还构建了一个现代大型语言模型（LLMs）的演化树，以追踪近年来语言模型的发展，并突出了一些最知名的模型。
        这些资源旨在帮助从业者在大型语言模型（LLMs）及其在自然语言处理（NLP）应用中的应用方面进行导航。我们还根据模型和数据许可信息包括它们的使用限制。如果您发现我们仓库中的任何资源有帮助，请随意使用（不要忘记引用我们的论文！���）。我们欢迎拉取请求来完善这个图表！

GitHub：GitHub - Mooler0410/LLMsPracticalGuide: A curated list of practical guide resources of LLMs (LLMs Tree, Examples, Papers)

一、模型的实用指南：BERT-style/GPT-style

1.1、BERT-style 的语言模型：编码器-解码器或仅编码器

BERT BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding, 2018, Paper RoBERTa RoBERTa: A Robustly Optimized BERT Pretraining Approach, 2019, Paper DistilBERT DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter, 2019, Paper ALBERT ALBERT: A Lite BERT for Self-supervised Learning of Language Representations, 2019, Paper UniLM Unified Language Model Pre-training for Natural Language Understanding and Generation, 2019 Paper ELECTRA ELECTRA: PRE-TRAINING TEXT ENCODERS AS DISCRIMINATORS RATHER THAN GENERATORS, 2020, Paper T5 "Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer". Colin Raffel et al. JMLR 2019. Paper GLM "GLM-130B: An Open Bilingual Pre-trained Model". 2022. Paper AlexaTM "AlexaTM 20B: Few-Shot Learning Using a Large-Scale Multilingual Seq2Seq Model". Saleh Soltan et al. arXiv 2022. Paper ST-MoE ST-MoE: Designing Stable and Transferable Sparse Expert Models. 2022 Paper

BERT BERT：用于语言理解的深度双向转换器的预训练，2018年 RoBERTa RoBERTa：一种优化稳健的BERT预训练方法，2019年 DistilBERT DistilBERT：BERT的精简版本：更小、更快、更便宜、更轻巧，2019年 ALBERT ALBERT：一种轻量级的自监督学习语言表示方法，2019年 UniLM 统一语言模型预训练用于自然语言理解和生成，2019年 ELECTRA ELECTRA：将文本编码器作为判别器而非生成器进行预训练，2020年 T5 "探索具有统一文本到文本Transformer的迁移学习的极限"。Colin Raffel等人，JMLR 2019年 GLM "GLM-130B：一种开放的双语预训练模型"，2022年 AlexaTM "AlexaTM 20B：使用大规模多语言Seq2Seq模型进行少样本学习"。Saleh Soltan等人，arXiv 2022年 ST-MoE ST-MoE：设计稳定且可转移的稀疏专家模型。2022年

1.2、GPT-style的语言模型：仅解码器

GPT Improving Language Understanding by Generative Pre-Training. 2018. Paper GPT-2 Language Models are Unsupervised Multitask Learners. 2018. Paper GPT-3 "Language Models are Few-Shot Learners". NeurIPS 2020. Paper OPT "OPT: Open Pre-trained Transformer Language Models". 2022. Paper PaLM "PaLM: Scaling Language Modeling with Pathways". Aakanksha Chowdhery et al. arXiv 2022. Paper BLOOM "BLOOM: A 176B-Parameter Open-Access Multilingual Language Model". 2022. Paper MT-NLG "Using DeepSpeed and Megatron to Train Megatron-Turing NLG 530B, A Large-Scale Generative Language Model". 2021. Paper GLaM "GLaM: Efficient Scaling of Language Models with Mixture-of-Experts". ICML 2022. Paper Gopher "Scaling Language Models: Methods, Analysis & Insights from Training Gopher". 2021. Paper chinchilla "Training Compute-Optimal Large Language Models". 2022. Paper LaMDA "LaMDA: Language Models for Dialog Applications". 2021. Paper LLaMA "LLaMA: Open and Efficient Foundation Language Models". 2023. Paper GPT-4 "GPT-4 Technical Report". 2023. Paper BloombergGPT BloombergGPT: A Large Language Model for Finance, 2023, Paper GPT-NeoX-20B: "GPT-NeoX-20B: An Open-Source Autoregressive Language Model". 2022. Paper

GPT 通过生成式预训练改进语言理解。2018年 GPT-2 语言模型是无监督多任务学习者。2018年 GPT-3 "语言模型是少样本学习者"。NeurIPS 2020年 OPT "OPT：开放预训练转换器语言模型"。2022年 PaLM "PaLM：通过路径扩展语言建模"。Aakanksha Chowdhery等人，arXiv 2022年 BLOOM "BLOOM：一种拥有176B参数的开放式多语言语言模型"。2022年 MT-NLG "使用DeepSpeed和Megatron训练Megatron-Turing NLG 530B，一种大规模生成语言模型"。2021年 GLaM "GLaM：通过专家混合实现语言模型的高效扩展"。ICML 2022年 Gopher "扩展语言模型：方法、分析和Gopher训练见解"。2021年 chinchilla "训练计算优化的大型语言模型"。2022年 LaMDA "LaMDA：用于对话应用的语言模型"。2021年 LLaMA "LLaMA：开放和高效的基础语言模型"。2023年 GPT-4 "GPT-4技术报告"。2023年 BloombergGPT BloombergGPT：一种面向金融领域的大型语言模型，2023年 GPT-NeoX-20B："GPT-NeoX-20B：一种开源自回归语言模型"。2022年

二、数据实用指南：预训练数据/微调数据/测试数据

2.1、预训练数据

RedPajama, 2023. Repo The Pile: An 800GB Dataset of Diverse Text for Language Modeling, Arxiv 2020. Paper How does the pre-training objective affect what large language models learn about linguistic properties?, ACL 2022. Paper Scaling laws for neural language models, 2020. Paper Data-centric artificial intelligence: A survey, 2023. Paper How does GPT Obtain its Ability? Tracing Emergent Abilities of Language Models to their Sources, 2022. Blog

RedPajama，2023年，代码库 The Pile：一份用于语言建模的800GB多样化文本数据集，Arxiv 2020年 预训练目标如何影响大型语言模型对语言属性的学习？ACL 2022年 神经语言模型的扩展定律，2020年 以数据为中心的人工智能：一份调查，2023年 GPT如何获得其能力？追踪语言模型的新兴能力源自何处，2022年，博客

2.2、微调数据

Benchmarking zero-shot text classification: Datasets, evaluation and entailment approach, EMNLP 2019. Paper Language Models are Few-Shot Learners, NIPS 2020. Paper Does Synthetic Data Generation of LLMs Help Clinical Text Mining? Arxiv 2023 Paper

基于零样本文本分类的基准测试：数据集、评估和蕴含方法，EMNLP 2019年 语言模型是少样本学习者，NIPS 2020年 合成数据生成对临床文本挖掘有帮助吗？Arxiv 2023年

2.3、测试数据/用户数据

Shortcut learning of large language models in natural language understanding: A survey, Arxiv 2023. Paper On the Robustness of ChatGPT: An Adversarial and Out-of-distribution Perspective Arxiv, 2023. Paper SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systems Arxiv 2019. Paper

自然语言理解中大型语言模型的快捷学习：一份调查，Arxiv 2023年 关于ChatGPT的鲁棒性：从对抗和分布外的角度看，Arxiv 2023年 SuperGLUE：一项用于通用语言理解系统的更具挑战性的基准测试，Arxiv 2019年

三、自然语言处理任务的实用指南：NLP五大任务+效率+可信度+基准指令调优+对齐

我们为用户的自然语言处理（NLP）应用程序构建了一个决策流程，用于选择LLMs或经过微调的模型\protect\footnotemark。该决策流程帮助用户评估他们手头的下游NLP应用程序是否满足特定条件，并基于该评估确定LLMs或经过微调的模型是否是他们应用程序的最佳选择。

3.1、传统的NLU任务

A benchmark for toxic comment classification on civil comments dataset Arxiv 2023 Paper Is chatgpt a general-purpose natural language processing task solver? Arxiv 2023Paper Benchmarking large language models for news summarization Arxiv 2022 Paper

Civil Comments数据集上有害评论分类的基准 Arxiv 2023  ChatGPT是否是一个通用的自然语言处理任务求解器？Arxiv 2023  大型语言模型在新闻摘要中的基准测试Arxiv 2022

3.2、生成任务

News summarization and evaluation in the era of gpt-3 Arxiv 2022 Paper Is chatgpt a good translator? yes with gpt-4 as the engine Arxiv 2023 Paper Multilingual machine translation systems from Microsoft for WMT21 shared task, WMT2021 Paper Can ChatGPT understand too? a comparative study on chatgpt and fine-tuned bert, Arxiv 2023, Paper

在GPT-3时代的新闻摘要和评估 Arxiv 2022  ChatGPT是否是一个好的翻译器？是的，使用GPT-4作为引擎Arxiv 2023   微软的多语言机器翻译系统用于WMT21共享任务，WMT2021   ChatGPT能够理解吗？关于ChatGPT和经过微调的BERT的对比研究Arxiv 2023

3.3、知识密集型任务

Measuring massive multitask language understanding, ICLR 2021 Paper Beyond the imitation game: Quantifying and extrapolating the capabilities of language models, Arxiv 2022 Paper Inverse scaling prize, 2022 Link Atlas: Few-shot Learning with Retrieval Augmented Language Models, Arxiv 2022 Paper Large Language Models Encode Clinical Knowledge, Arxiv 2022 Paper

测量大规模多任务语言理解 ICLR 2021  超越模仿游戏：量化和推断语言模型的能力 Arxiv 2022  Inverse scaling prize, 2022 链接 Atlas: 带有检索增强的少样本学习语言模型Arxiv 2022   大型语言模型编码临床知识，Arxiv 2022

3.4、随着规模增长的能力

Training Compute-Optimal Large Language Models, NeurIPS 2022 Paper Scaling Laws for Neural Language Models, Arxiv 2020 Paper Solving math word problems with process- and outcome-based feedback, Arxiv 2022 Paper Chain of thought prompting elicits reasoning in large language models, NeurIPS 2022 Paper Emergent abilities of large language models, TMLR 2022 Paper Inverse scaling can become U-shaped, Arxiv 2022 Paper Towards Reasoning in Large Language Models: A Survey, Arxiv 2022 Paper

训练计算优化的大型语言模型 NeurIPS 2022  神经网络语言模型的缩放定律，Arxiv 2020   使用基于过程和结果的反馈解决数学问题，Arxiv 2022   思维链引导引发大型语言模型的推理，NeurIPS 2022   大型语言模型的新兴能力，TMLR 2022   Inverse scaling可能呈U形，Arxiv 2022  走向大型语言模型的推理：一项调查，Arxiv 2022

3.5、特定任务

Image as a Foreign Language: BEiT Pretraining for All Vision and Vision-Language Tasks, Arixv 2022 Paper PaLI: A Jointly-Scaled Multilingual Language-Image Model, Arxiv 2022 Paper AugGPT: Leveraging ChatGPT for Text Data Augmentation, Arxiv 2023 Paper Is gpt-3 a good data annotator?, Arxiv 2022 Paper Want To Reduce Labeling Cost? GPT-3 Can Help, EMNLP findings 2021 Paper GPT3Mix: Leveraging Large-scale Language Models for Text Augmentation, EMNLP findings 2021 Paper LLM for Patient-Trial Matching: Privacy-Aware Data Augmentation Towards Better Performance and Generalizability, Arxiv 2023 Paper ChatGPT Outperforms Crowd-Workers for Text-Annotation Tasks, Arxiv 2023 Paper G-Eval: NLG Evaluation using GPT-4 with Better Human Alignment, Arxiv 2023 Paper GPTScore: Evaluate as You Desire, Arxiv 2023 Paper Large Language Models Are State-of-the-Art Evaluators of Translation Quality, Arxiv 2023 Paper Is ChatGPT a Good NLG Evaluator? A Preliminary Study, Arxiv 2023 Paper

图像作为外语：针对所有视觉和视觉-语言任务的BEiT预训练模型 Arixv 2022  PaLI：一个联合缩放的多语言语言-图像模型，Arxiv 2022   AugGPT：利用ChatGPT进行文本数据增强 Arxiv 2023  GPT-3是否是一个好的数据标注器？Arxiv 2022   想要降低标注成本吗？GPT-3可以帮助，EMNLP findings 2021   GPT3Mix：利用大规模语言模型进行文本增强，EMNLP findings 2021   LLM用于患者-试验匹配：隐私感知的数据增强以提高性能和普适性，Arxiv 2023   ChatGPT在文本注释任务中胜过众包工作者，Arxiv 2023   G-Eval：使用GPT-4进行自然语言生成评估，Arxiv 2023   GPTScore：根据需求进行评估，Arxiv 2023   大型语言模型是翻译质量的最先进评估器，Arxiv 2023   ChatGPT是一个好的自然语言生成评估器吗？初步研究，Arxiv 2023

3.6、现实世界的任务

Sparks of Artificial General Intelligence: Early experiments with GPT-4, Arxiv 2023 Paper

人工通用智能的火花：GPT-4的初步实验 Arxiv 2023

3.7、效率Efficiency

3.7.1、成本Cost

Openai’s gpt-3 language model: A technical overview, 2020. Blog Post Measuring the carbon intensity of ai in cloud instances, FaccT 2022. Paper In AI, is bigger always better?, Nature Article 2023. Article Language Models are Few-Shot Learners, NeurIPS 2020. Paper Pricing, OpenAI. Blog Post

OpenAI的GPT-3语言模型：技术概述，2020年博客文章  测量云计算实例中人工智能的碳强度，FaccT 2022   在人工智能领域，更大是否总是更好？，2023年自然文章  语言模型是少样本学习器，NeurIPS 2020   定价，OpenAI博客文章

3.7.2、延迟Latency

HELM: Holistic evaluation of language models, Arxiv 2022. Paper

HELM：语言模型的全面评估，Arxiv 2022

3.7.3、参数高效微调Parameter-Efficient Fine-Tuning

LoRA: Low-Rank Adaptation of Large Language Models, Arxiv 2021. Paper Prefix-Tuning: Optimizing Continuous Prompts for Generation, ACL 2021. Paper P-Tuning: Prompt Tuning Can Be Comparable to Fine-tuning Across Scales and Tasks, ACL 2022. Paper P-Tuning v2: Prompt Tuning Can Be Comparable to Fine-tuning Universally Across Scales and Tasks, Arxiv 2022. Paper

LoRA：大型语言模型的低秩适应，Arxiv 2021   Prefix-Tuning：优化生成任务的连续提示，ACL 2021   P-Tuning：在规模和任务上，提示调优可以与微调相媲美，ACL 2022   P-Tuning v2：在各种规模和任务上，提示调优可以与微调普遍相媲美，Arxiv 2022

3.7.4、预训练系统Pretraining System

ZeRO: Memory Optimizations Toward Training Trillion Parameter Models, Arxiv 2019. Paper Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism, Arxiv 2019. Paper Efficient Large-Scale Language Model Training on GPU Clusters Using Megatron-LM, Arxiv 2021. Paper Reducing Activation Recomputation in Large Transformer Models, Arxiv 2021. Paper

ZeRO：向训练万亿参数模型进行内存优化，Arxiv 2019   Megatron-LM：使用模型并行性训练数十亿参数的语言模型，Arxiv 2019   使用Megatron-LM在GPU集群上进行高效的大规模语言模型训练，Arxiv 2021   减少大型Transformer模型中的激活重新计算，Arxiv 2021

3.8、可信度Trustworthiness

3.8.1、鲁棒性和校准性Robustness and Calibration

Calibrate before use: Improving few-shot performance of language models, ICML 2021. Paper SPeC: A Soft Prompt-Based Calibration on Mitigating Performance Variability in Clinical Notes Summarization, Arxiv 2023. Paper

在使用之前进行校准：改善语言模型的少样本性能，ICML 2021   SPeC：软提示为基础的临床记录摘要中的校准，缓解性能变异性，Arxiv 2023

3.8.2、虚假偏见Spurious biases

Shortcut learning of large language models in natural language understanding: A survey, 2023 Paper Mitigating gender bias in captioning system, WWW 2020 Paper Calibrate Before Use: Improving Few-Shot Performance of Language Models, ICML 2021 Paper Shortcut Learning in Deep Neural Networks, Nature Machine Intelligence 2020 Paper Do Prompt-Based Models Really Understand the Meaning of Their Prompts?, NAACL 2022 Paper

自然语言理解中大型语言模型的快捷学习：一项调查，2023   减轻字幕系统中的性别偏见，WWW 2020   在使用之前进行校准：改善语言模型的少样本性能，ICML 2021   深度神经网络的快捷学习，Nature Machine Intelligence 2020   基于提示的模型真的理解其提示的含义吗？，NAACL 2022

3.8.3、安全问题Safety issues

GPT-4 System Card, 2023 Paper The science of detecting llm-generated texts, Arxiv 2023 Paper How stereotypes are shared through language: a review and introduction of the aocial categories and stereotypes communication (scsc) framework, Review of Communication Research, 2019 Paper Gender shades: Intersectional accuracy disparities in commercial gender classification, FaccT 2018 Paper

GPT-4系统卡片，2023   检测LLM生成文本的科学，Arxiv 2023   通过语言分享刻板印象：社会类别和刻板印象传播（SCSC）框架的回顾和介绍，Review of Communication Research，2019   性别影子：商业性别分类中的交叉准确性差异，FaccT 2018

3.9、基准指令调优Benchmark Instruction Tuning

FLAN: Finetuned Language Models Are Zero-Shot Learners, Arxiv 2021 Paper T0: Multitask Prompted Training Enables Zero-Shot Task Generalization, Arxiv 2021 Paper Cross-task generalization via natural language crowdsourcing instructions, ACL 2022 Paper Tk-INSTRUCT: Super-NaturalInstructions: Generalization via Declarative Instructions on 1600+ NLP Tasks, EMNLP 2022 Paper FLAN-T5/PaLM: Scaling Instruction-Finetuned Language Models, Arxiv 2022 Paper The Flan Collection: Designing Data and Methods for Effective Instruction Tuning, Arxiv 2023 Paper OPT-IML: Scaling Language Model Instruction Meta Learning through the Lens of Generalization, Arxiv 2023 Paper

FLAN：微调语言模型是零样本学习器，Arxiv 2021   T0：多任务提示训练实现零样本任务泛化，Arxiv 2021   通过自然语言众包指令实现跨任务泛化，ACL 2022   Tk-INSTRUCT：超自然指令：通过1600+ NLP任务的声明性指令实现泛化，EMNLP 2022   FLAN-T5/PaLM：扩展指令微调语言模型，Arxiv 2022   Flan集合：设计数据和方法以实现有效的指令调优，Arxiv 2023   OPT-IML：通过泛化视角扩展语言模型指令元学习，Arxiv 2023

3.10、对齐Alignment

Deep Reinforcement Learning from Human Preferences, NIPS 2017 Paper Learning to summarize from human feedback, Arxiv 2020 Paper A General Language Assistant as a Laboratory for Alignment, Arxiv 2021 Paper Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback, Arxiv 2022 Paper Teaching language models to support answers with verified quotes, Arxiv 2022 Paper InstructGPT: Training language models to follow instructions with human feedback, Arxiv 2022 Paper Improving alignment of dialogue agents via targeted human judgements, Arxiv 2022 Paper Scaling Laws for Reward Model Overoptimization, Arxiv 2022 Paper Scalable Oversight: Measuring Progress on Scalable Oversight for Large Language Models, Arxiv 2022 Paper

从人类偏好中进行深度强化学习，NIPS 2017   从人类反馈中学习总结，Arxiv 2020   作为对齐实验室的通用语言助手，Arxiv 2021   通过从人类反馈中进行强化学习训练一个有帮助且无害的助手，Arxiv 2022   教导语言模型通过验证的引用支持答案，Arxiv 2022   InstructGPT：通过人类反馈训练语言模型遵循指令，Arxiv 2022   通过有针对性的人类判断提高对话代理的对齐，Arxiv 2022   奖励模型过度优化的规模定律，Arxiv 2022   可扩展监督：衡量大型语言模型可扩展监督的进展，Arxiv 2022

3.10.1、安全对齐（无害）Safety Alignment (Harmless)

Red Teaming Language Models with Language Models, Arxiv 2022 Paper Constitutional ai: Harmlessness from ai feedback, Arxiv 2022 Paper The Capacity for Moral Self-Correction in Large Language Models, Arxiv 2023 Paper OpenAI: Our approach to AI safety, 2023 Blog

使用语言模型对抗语言模型，Arxiv 2022   宪法AI：通过AI反馈实现无害性，Arxiv 2022   大型语言模型的道德自我修正能力，Arxiv 2023   OpenAI：我们对AI安全的方法，2023博客

3.10.2、真实对齐（诚实） Truthfulness Alignment (Honest)

Reinforcement Learning for Language Models, 2023 Blog

语言模型的强化学习，2023博客

3.10.3、指导实践（有帮助）Practical Guides for Prompting (Helpful)

OpenAI Cookbook. Blog Prompt Engineering. Blog ChatGPT Prompt Engineering for Developers! Course

OpenAI食谱。博客 提示工程。博客 开发人员的ChatGPT提示工程！课程

3.10.4、开源社区的对齐努力Alignment Efforts of Open-source Communtity

Self-Instruct: Aligning Language Model with Self Generated Instructions, Arxiv 2022 Paper Alpaca. Repo Vicuna. Repo Dolly. Blog DeepSpeed-Chat. Blog GPT4All. Repo OpenAssitant. Repo ChatGLM. Repo MOSS. Repo Lamini. Repo/Blog

自我指导：用自动生成的指令对齐语言模型，Arxiv 2022

四、三大类模型的使用和限制：Encoder-only、Encoder-Decoder、Decoder-only

我们从模型及其预训练数据的角度提供信息。我们敦促社区中的用户参考公共模型和数据的许可信息，并负责任地使用它们。我们敦促开发者特别注意许可证的问题，使其透明且全面，以防止任何不必要和意外的使用情况。

4.1、Encoder-only

LLMs	Model			Data
	License	Commercial Use	Other noteable restrictions	License	Corpus
BERT series of models (general domain)	Apache 2.0	✅		Public	BooksCorpus, English Wikipedia
RoBERTa	MIT license	✅		Public	BookCorpus, CC-News, OpenWebText, STORIES
ERNIE	Apache 2.0	✅		Public	English Wikipedia
SciBERT	Apache 2.0	✅		Public	BERT corpus, 1.14M papers from Semantic Scholar
LegalBERT	CC BY-SA 4.0	❌		Public (except data from the Case Law Access Project)	EU legislation, US court cases, etc.
BioBERT	Apache 2.0	✅		PubMed	PubMed, PMC

4.2、Encoder-Decoder

	License	Commercial Use	Other noteable restrictions	License	Corpus
T5	Apache 2.0	✅		Public	C4
Flan-T5	Apache 2.0	✅		Public	C4, Mixture of tasks (Fig 2 in paper)
BART	Apache 2.0	✅		Public	RoBERTa corpus
GLM	Apache 2.0	✅		Public	BooksCorpus and English Wikipedia
ChatGLM	ChatGLM License	❌	No use for illegal purposes or military research, no harm the public interest of society	N/A	1T tokens of Chinese and English corpus

4.3、Decoder-only

	License	Commercial Use	Other noteable restrictions	License	Corpus
GPT2	Modified MIT License	✅	Use GPT-2 responsibly and clearly indicate your content was created using GPT-2.	Public	WebText
GPT-Neo	MIT license	✅		Public	Pile
GPT-J	Apache 2.0	✅		Public	Pile
---> Dolly	CC BY NC 4.0	❌		CC BY NC 4.0, Subject to terms of Use of the data generated by OpenAI	Pile, Self-Instruct
---> GPT4ALL-J	Apache 2.0	✅		Public	GPT4All-J dataset
Pythia	Apache 2.0	✅		Public	Pile
---> Dolly v2	MIT license	✅		Public	Pile, databricks-dolly-15k
OPT	OPT-175B LICENSE AGREEMENT	❌	No development relating to surveillance research and military, no harm the public interest of society	Public	RoBERTa corpus, the Pile, PushShift.io Reddit
---> OPT-IML	OPT-175B LICENSE AGREEMENT	❌	same to OPT	Public	OPT corpus, Extended version of Super-NaturalInstructions
YaLM	Apache 2.0	✅		Unspecified	Pile, Teams collected Texts in Russian
BLOOM	The BigScience RAIL License	✅	No use of generating verifiably false information with the purpose of harming others; content without expressly disclaiming that the text is machine generated	Public	ROOTS corpus (Lauren¸con et al., 2022)
---> BLOOMZ	The BigScience RAIL License	✅	same to BLOOM	Public	ROOTS corpus, xP3
Galactica	CC BY-NC 4.0	❌		N/A	The Galactica Corpus
LLaMA	Non-commercial bespoke license	❌	No development relating to surveillance research and military, no harm the public interest of society	Public	CommonCrawl, C4, Github, Wikipedia, etc.
---> Alpaca	CC BY NC 4.0	❌		CC BY NC 4.0, Subject to terms of Use of the data generated by OpenAI	LLaMA corpus, Self-Instruct
---> Vicuna	CC BY NC 4.0	❌		Subject to terms of Use of the data generated by OpenAI; Privacy Practices of ShareGPT	LLaMA corpus, 70K conversations from ShareGPT.com
---> GPT4ALL	GPL Licensed LLaMa	❌		Public	GPT4All dataset
OpenLLaMA	Apache 2.0	✅		Public	RedPajama
CodeGeeX	The CodeGeeX License	❌	No use for illegal purposes or military research	Public	Pile, CodeParrot, etc.
StarCoder	BigCode OpenRAIL-M v1 license	✅	No use of generating verifiably false information with the purpose of harming others; content without expressly disclaiming that the text is machine generated	Public	The Stack
MPT-7B	Apache 2.0	✅		Public	mC4 (english), The Stack, RedPajama, S2ORC
falcon	TII Falcon LLM License	✅/❌	Available under a license allowing commercial use	Public	RefinedWeb