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总结Prompt&LLM论文，开源数据&模型，AIGC应用

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DecryptPrompt

持续更新以下内容，Star to keep updated~

  1. 开源LLM
  2. 指令微调和RLHF数据以及训练框架
  3. Prompt和LLM相关论文按细分方向梳理
  4. AIGC相关应用
  5. Prompt指南和教程
  6. ChatGPT及AGI相关解读
  7. ChatGPT相关商业应用

My blogs & ChatGPT应用

模型和数据

国外模型

模型链接 模型描述
Google Bard 谷歌bard虽迟但到，可以申请waitlist了
Claude ChatGPT最大竞争对手Claude也开放申请了，slack中无限试用
LLaMA Meta开源指令微调LLM，规模70 亿到 650 亿不等
ChatLLaMA 基于RLHF微调了LLaMA
Alpaca 斯坦福开源的使用52k数据在7B的LLaMA上微调得到，
Alpaca-lora LORA微调的LLaMA
Vicuna Alpaca前成员等开源以LLama13B为基础使用ShareGPT指令微调的模型，提出了用GPT4来评测模型效果
MiniGPT4 Vicuna+BLIP2 文本视觉融合
StackLLama LLama使用Stackexchange数据+SFT+RL
Cerebras Cerebras开源了1亿到130亿的7个模型，从预训练数据到参数全开源
PaLM-E 谷歌多模态大模型，540B的PaLM语言模型和22B的ViT视觉模型相结合，得到562B的PaLM-E模型，在机器人应用场景有了新的突破
Dolly-v2 可商用 7b指令微调开源模型
MetaLM 微软开源的大规模自监督预训练模型
Amazon Titan 亚马逊在aws上增加自家大模型
OPT-IML Meta复刻GPT3，up to 175B, 不过效果并不及GPT3
Bloom BigScience出品，规模最大176B
BloomZ BigScience出品, 基于Bloom微调
Galacia 和Bloom相似，更针对科研领域训练的模型
T0 BigScience出品，3B~11B的在T5进行指令微调的模型

国内模型

模型链接 模型描述
ChatGLM 清华开源的、支持中英双语的对话语言模型，使用了代码训练，指令微调和RLHF。和以下GLM相同大小的130B的模型还在开发中。试用了下超出预期！
Moss 为复旦正名！开源了预训练，指令微调的全部数据和模型
Wombat-7B 达摩院开源无需强化学习使用RRHF对齐的语言模型
Chinese-LLaMA-Alpaca 哈工大中文指令微调的LLaMA
Luotuo 中文指令微调的LLaMA，和ChatGLM
文心一言 已经拿到邀请码并试用，虽然人格化程度显著低，但效果上并没有很拉胯，国产YYDS！不过商业化霸王条款确实不少
通义千问 阿里系LLM开放申请，还没拿到邀请码，等待ing
Phoenix 港中文开源凤凰和奇美拉LLM，Bloom基座，40+语言支持
Guanaco LLama 7B基座，在alpaca52K数据上加入534K多语言指令数据微调
Chinese Vincuna LLama 7B基座，使用Belle+Guanaco数据训练
Baize 使用100k self-chat对话数据微调的LLama
BELLE 使用ChatGPT生成数据对开源模型进行中文优化
PromptCLUE 多任务Prompt语言模型
PLUG 阿里达摩院发布的大模型，提交申请会给下载链接
CPM2.0 智源发布CPM2.0
GLM 清华发布的中英双语130B预训练模型

指令微调&RL工具

工具描述 链接
LoRA：Low-Rank指令微调方案 https://github.com/tloen/alpaca-lora
peft：parameter-efficient prompt tunnging工具集 https://github.com/huggingface/peft
RL4LMs：AllenAI的RL工具 https://github.com/allenai/RL4LMs
trl：基于Transformer的强化训练框架 https://github.com/lvwerra/trl
trlx：分布式训练trl https://github.com/CarperAI/trlx
RL4LMs：AllenAI的RL工具 https://github.com/allenai/RL4LMs
LMFlow：港科大实验室开源的大模型微调框架，支持以上多数开源模型的指令微调和RLHF https://github.com/OptimalScale/LMFlow
Deepspeed：针对RL训练和推理的整合优化 https://github.com/microsoft/DeepSpeed
langchain：LLM工具集 https://github.com/hwchase17/langchain
BMTTools: 清华出品类似langchain https://github.com/OpenBMB/BMTools
BabyAGI：自执行LLM Agent https://github.com/yoheinakajima/babyagi
AutoGPT：自执行LLM Agent https://github.com/Torantulino/Auto-GPT
Jarvis: 大模型调用小模型框架，给小模型一个未来！ https://github.com/search?q=jarvis

开源数据

无敌：https://github.com/PhoebusSi/Alpaca-CoT 分类归纳整理了众多数据集并统一了数据格式，附Huggingface Data, 无脑点赞！

数据类型 数据描述 数据链接
指令微调 self-instruct，GPT3自动生成&过滤得到指令集 https://github.com/yizhongw/self-instruct
指令微调 Standford Alpaca：52K text-davinci-003生成的self-instruct指令数据集 https://github.com/tatsu-lab/stanford_alpaca
指令微调 中文翻译Alpaca还有一些其他指令数据集 https://github.com/hikariming/alpaca_chinese_dataset https://github.com/carbonz0/alpaca-chinese-dataset
指令微调 Guanaco数据：对Alphca指令重写后以不同语言生成总共534K，有对话和非对话类型 https://huggingface.co/datasets/JosephusCheung/GuanacoDataset
指令微调 InstructWild数据：直接用中文种子Self-Instruct生成 https://github.com/XueFuzhao/InstructionWild/tree/main/data
指令微调 BELLE100万指令数据，参考Alpaca用ChatGPT生成 https://github.com/LianjiaTech/BELLE
指令微调 PromptCLUE多任务提示数据集：模板构建，只包含标准NLP任务 https://github.com/CLUEbenchmark/pCLUE
指令微调 TK-Instruct微调用的指令数据集, 全人工标注1600+NLP任务 https://instructions.apps.allenai.org/
指令微调 T0微调用的指令数据集（P3） https://huggingface.co/datasets/bigscience/P3
指令微调 p3衍生的46种多语言数据集（xmtf） https://github.com/bigscience-workshop/xmtf
指令微调 Unnatural Instruction使用GPT3生成后改写得到240k https://github.com/orhonovich/unnatural-instructions
指令微调 中文指令微调数据集包括翻译alpaca，多轮对话，考试指令 https://github.com/BAAI-Zlab/COIG
对话指令 LAION 策划的开放指令通用数据集中手动选择的组件子集 已开源40M 3万个,100M在路上 https://github.com/LAION-AI/Open-Instruction-Generalist
对话指令 Vicuna训练使用的样本，用API获取了sharegpt上用户和chatgpt对话历史 https://github.com/domeccleston/sharegpt
对话指令 Baize基于Chat GPT构建的self-chat数据 https://github.com/project-baize/baize-chatbot/tree/main/data
对话指令 FaceBook开源BlenderBot训练对话数据~6K https://huggingface.co/datasets/blended_skill_talk
对话指令 AllenAI开源38.5万个对话高质量数据集SODA https://realtoxicityprompts.apps.allenai.org/
对话指令 InstructDial在单一对话任务类型上进行指令微调 https://github.com/prakharguptaz/Instructdial
RLHF Anthropic hh-rlhf数据集 https://huggingface.co/datasets/Anthropic/hh-rlhf
RLHF Stack-exchange上问题对应多个答案，每个答案有打分 https://huggingface.co/datasets/HuggingFaceH4/stack-exchange-preferences/tree/main
RLHF Facebook Bot Adversarial Dialogues数据集5K https://github.com/facebookresearch/ParlAI
RLHF AllenAI Real Toxicity prompts https://github.com/facebookresearch/ParlAI
RLHF OpenAssistant Conversations 160K消息，13500人工生成, 英文为主 https://huggingface.co/datasets/OpenAssistant/oasst1
评估集 BigBench(Beyond the Imitation Game Benchmark) https://github.com/google/BIG-bench
评估集 Complex QA：用于ChatGPT的评测指令集 https://github.com/tan92hl/Complex-Question-Answering-Evaluation-of-ChatGPT
评估集 Langchain开源评估数据集 https://huggingface.co/LangChainDatasets
预训练 RedPajama开源的复刻llama的预训练数据集 https://github.com/togethercomputer/RedPajama-Data
多源数据集整合 opendatalab整合了预训练阶段的多个数据源 https://opendatalab.org.cn/?industry=9821&source=JUU3JTlGJUE1JUU0JUI5JThF

Resources

Tools & Tutorial

AIGC playground

  • cognosys: 全网最火的web端AutoGPT，不过咋说呢试用了下感觉下巴要笑掉了，不剧透去试试你就知道
  • godmode：需要人为每一步交互的的AutoGPT
  • agentgpt: 基础AutoGPT
  • New Bing：需要连外网否则会重定向到bing中国，需要申请waitlist ⭐
  • Perplexity.ai: 同样需要科学上网，感觉比Bing做的更好的接入ChatGPT的神奇搜索引擎，在Bing之外还加入了相关推荐和追问 ⭐
  • BingGPT: NewBing开源桌面客户端，可以将聊天记录导出
  • DocsGPT: 把ChatGPT开放域问答转化成封闭域问答的通用方案，试用垂类领域问答场景,可以试用定制的ChatBot ⭐
  • langchain-ChatGLM: 基于ChatGLM的本地知识问答，和上面的DocsGPT相似，不过可以本地部署:star:
  • ChatPDF: 国内的ChatPDF, 上传pdf后，会给出文章的Top5可能问题，然后对话式从文档中进行问答和检索，10s读3万字
  • ChatDoc:ChatPDF升级版，增加了表格类解析，和完善的索引引用加跳转加对应文章内容高亮，哈哈我准备自己整一个
  • ChatPaper: 根据输入关键词，自动在arxiv上下载最新的论文，并对论文进行摘要总结，可以在huggingface上试用！
  • OpenRead: 面向论文写作，阅读场景，可以帮助生成文献综述，以及提供和NotionAI相似的智能Markdown用于写作
  • researchgpt: 和ChatPDF类似，支持arivx论文下载，加载后对话式获取论文重点
  • BriefGPT: 日更Arxiv论文，并对论文进行摘要，关键词抽取，帮助研究者了解最新动态, UI不错哟
  • ChatGPT-academic: 又是一个基于gradio实现的paper润色，摘要等功能打包的实现
  • feishu-chatgpt: 飞书chatgpt，和365copilot相似也是多组件集成, 有点全！
  • ChatMind: chatgpt生成思维导图，针对话题的生成还可以，但是针对某本书的就是瞎编了，但是感觉和检索式阅读方式结合效果会出彩~
  • Shell: 基于ChatGPT的AI英语聊天工具，口语练习助手
  • AI Topiah: 聆心智能AI角色聊天，和路飞唠了两句，多少有点中二之魂在燃烧
  • chatbase: 情感角色聊天，还没尝试
  • Vana: virtual DNA, 通过聊天创建虚拟自己！概念很炫
  • WriteSonic：AI写作，支持对话和定向创作如广告文案，商品描述, 支持Web检索是亮点，支持中文
  • copy.ai: WriteSonic竞品，亮点是像论文引用一样每句话都有对应网站链接，可以一键复制到右边的创作Markdown，超级好用！ ⭐
  • NotionAI：智能Markdown，适用真相！在创作中用command调用AI辅助润色，扩写，检索内容，给创意idea
  • Jasper: 同上，全是竞品哈哈
  • copy.down: 中文的营销文案生成，只能定向创作，支持关键词到文案的生成
  • ChatExcel: 指令控制excel计算，对熟悉excel的有些鸡肋，对不熟悉的有点用
  • ChatPPT: 使用ChatGPT进行PPT制作
  • BibiGPT: Bilibli视频内容一键总结，多模态文档
  • Microsoft 365 Copilot：微软Office全面接入GPT4，智能PPT，Excel，Word，暂无链接。其实就是上面开源创意的全家桶套餐
  • Google Workspace: 谷歌推出的搭载各种AI服务的办公场景全覆盖，暂无使用方案。
  • Copilot: 要付费哟
  • Fauxpilot: copilot本地开源替代
  • CodeGex: 国内替代品，还没试过
  • Codeium: Copilot替代品，有免费版本支持各种plugin
  • Wolverine: 代码自我debug的python脚本
  • dreamstudio.ai: 开创者，Stable Difussion， 有试用quota
  • midjourney: 开创者，艺术风格为主
  • Dall.E: 三巨头这就凑齐了
  • ControlNet: 为绘画创作加持可控性
  • GFPGAN: 照片修复
  • Visual ChatGPT: 微软发布图像ChatGPT，对话方式进行图像生成编辑，问答 ⭐
  • gemo.ai: 多模态聊天机器人，包括文本，图像，视频生成

Recommend Blog

ChatGPT 商用场景

  1. 搜索：Bing搜索，所向披靡; 360搜索，红孩儿，推理有限，支持国产
  2. 教育：Quizlet，私人教辅，你来学习，它来出题; 网易有道，子曰，AI口语老师，作业批改
  3. 电商：shopify，私人导购，情人节给女友买点啥？问它; Instcart, 私人营养师，搭配食谱，相关商品直接加购物车
  4. 金融：HSBC，使用大模型在财务信息汇总和分类；MorganStanley，
  5. BI: Salesforce, Slack使用AI回复，会议总结;
  6. 社交：Soul, 数字人，媒体内容自动生成；腾讯，QQ未来会融入ChatGPT
  7. 社区问答：CSDN, ChitGPT帮助提问者快速生成答案; Quora，接入了多个对话机器人进行问答
  8. 知识产权

Papers

paper List

Survey

  • A Survey of Large Language Models
  • Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processing ⭐
  • Paradigm Shift in Natural Language Processing
  • Pre-Trained Models: Past, Present and Future

LLM Ability Analysis & Probing

  • Sparks of Artificial General Intelligence: Early experiments with GPT-4
  • How does in-context learning work? A framework for understanding the differences from traditional supervised learning
  • Why can GPT learn in-context? Language Model Secretly Perform Gradient Descent as Meta-Optimizers
  • Emerging Ability of Large Language Models
  • Rethinking the Role of Demonstrations What Makes incontext learning work?
  • Can Explanations Be Useful for Calibrating Black Box Models

Tunning Free Prompt

  • GPT2: Language Models are Unsupervised Multitask Learners
  • GPT3: Language Models are Few-Shot Learners ⭐
  • LAMA: Language Models as Knowledge Bases?
  • AutoPrompt: Eliciting Knowledge from Language Models

Fix-Prompt LM Tunning

  • T5: Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
  • PET-TC(a): Exploiting Cloze Questions for Few Shot Text Classification and Natural Language Inference ⭐
  • PET-TC(b): PETSGLUE It’s Not Just Size That Matters Small Language Models are also few-shot learners
  • GenPET: Few-Shot Text Generation with Natural Language Instructions
  • LM-BFF: Making Pre-trained Language Models Better Few-shot Learners ⭐
  • ADEPT: Improving and Simplifying Pattern Exploiting Training

Fix-LM Prompt Tunning

  • Prefix-tuning: Optimizing continuous prompts for generation
  • Prompt-tunning: The power of scale for parameter-efficient prompt tuning ⭐
  • P-tunning: GPT Understands Too ⭐
  • WARP: Word-level Adversarial ReProgramming

LM + Prompt Tunning

  • P-tunning v2: Prompt Tuning Can Be Comparable to Fine-tunning Universally Across Scales and Tasks
  • PTR: Prompt Tuning with Rules for Text Classification
  • PADA: Example-based Prompt Learning for on-the-fly Adaptation to Unseen Domains

Fix-LM Adapter Tunning

  • LORA: LOW-RANK ADAPTATION OF LARGE LANGUAGE MODELS ⭐
  • LST: Ladder Side-Tuning for Parameter and Memory Efficient Transfer Learning
  • Parameter-Efficient Transfer Learning for NLP
  • INTRINSIC DIMENSIONALITY EXPLAINS THE EFFECTIVENESS OF LANGUAGE MODEL FINE-TUNING

Instruction Tunning LLMs

  • Flan: FINETUNED LANGUAGE MODELS ARE ZERO-SHOT LEARNERS ⭐
  • Flan-T5: Scaling Instruction-Finetuned Language Models
  • Instruct-GPT: Training language models to follow instructions with human feedback star:
  • T0: MULTITASK PROMPTED TRAINING ENABLES ZERO-SHOT TASK GENERALIZATION
  • Natural Instructions: Cross-Task Generalization via Natural Language Crowdsourcing Instructions
  • Tk-INSTRUCT: SUPER-NATURALINSTRUCTIONS: Generalization via Declarative Instructions on 1600+ NLP Tasks
  • Unnatural Instructions: Tuning Language Models with (Almost) No Human Labor
  • BELLE: Exploring the Impact of Instruction Data Scaling on Large Language Models: An Empirical Study on Real-World Use Cases
  • Baize: Baize: An Open-Source Chat Model with Parameter-Efficient Tuning on Self-Chat Data

Train for Dialogue

  • LaMDA: Language Models for Dialog Applications
  • Sparrow: Improving alignment of dialogue agents via targeted human judgements star:
  • BlenderBot 3: a deployed conversational agent that continually learns to responsibly engage
  • How NOT To Evaluate Your Dialogue System: An Empirical Study of Unsupervised Evaluation Metrics for Dialogue Response Generation

Chain of Thought

  • Chain of Thought Prompting Elicits Reasoning in Large Language Models ⭐
  • COMPLEXITY-BASED PROMPTING FOR MULTI-STEP REASONING
  • SELF-CONSISTENCY IMPROVES CHAIN OF THOUGHT REASONING IN LANGUAGE MODELS
  • Large Language Models are Zero-Shot Reasoners
  • PaLM: Scaling Language Modeling with Pathways

RLHF

  • Deepmind
    • Teaching language models to support answers with verified quotes
    • sparrow, Improving alignment of dialogue agents via targetd human judgements ⭐
  • openai
    • PPO: Proximal Policy Optimization Algorithms ⭐
    • Deep Reinforcement Learning for Human Preference
    • Fine-Tuning Language Models from Human Preferences
    • learning to summarize from human feedback
    • InstructGPT: Training language models to follow instructions with human feedback ⭐
    • Scaling Laws for Reward Model Over optimization ⭐
  • Anthropic
    • A General Language Assistant as a Laboratory for Alignmen
    • Red Teaming Language Models to Reduce Harms Methods,Scaling Behaviors and Lessons Learned
    • Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback
    • Constitutional AI Harmlessness from AI Feedback ⭐
  • AllenAI, RL4LM：IS REINFORCEMENT LEARNING (NOT) FOR NATURAL LANGUAGE PROCESSING BENCHMARKS

Agent: 让模型使用工具

  • Tool Former: Toolformer: Language Models Can Teach Themselves to Use Tools
  • MRKL SystemsA modular, neuro-symbolic architecture that combines large language models, external knowledge sources and discrete reasoning ⭐
  • ReAct: SYNERGIZING REASONING AND ACTING IN LANGUAGE MODELS ⭐
  • Self: MEASURING AND NARROWING THE COMPOSITIONALITY GAP IN LANGUAGE MODELS
  • PAL: Program-aided Language Models

指令数据生成

  • APE: LARGE LANGUAGE MODELS ARE HUMAN-LEVEL PROMPT ENGINEERS ⭐
  • SELF-INSTRUCT: Aligning Language Model with Self Generated Instructions ⭐
  • iPrompt: Explaining Data Patterns in Natural Language via Interpretable Autoprompting
  • Flipped Learning: Guess the Instruction! Flipped Learning Makes Language Models Stronger Zero-Shot Learners
  • Fairness-guided Few-shot Prompting for Large Language Models
  • Instruction induction: From few examples to natural language task descriptions.
  • Baize An Open-Source Chat Model with Parameter-Efficient Tuning on self-Chat Data

领域模型

  • BioGPT：Generative Pre-trained Transformer for Biomedical Text Generation and Mining
  • Galactia：A Large Language Model for Science
  • PubMed GPT: A Domain-specific large language model for biomedical text
  • BloombergGPT： A Large Language Model for Finance
  • ChatDoctor：Medical Chat Model Fine-tuned on LLaMA Model using Medical Domain Knowledge
  • Med-PaLM：Large Language Models Encode Clinical Knowledge[V1,V2] ⭐

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总结Prompt&LLM论文，开源数据&模型，AIGC应用

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