如果LLM的突然到来让你感到沮丧，不妨读下主目录的Choose Your Weapon Survival Strategies for Depressed AI Academics 持续更新以下内容，Star to keep updated~
目录顺序如下
- 国内外，垂直领域大模型
- Agent和指令微调等训练框架
- 开源指令，预训练，rlhf，对话，agent训练数据梳理
- AIGC相关应用
- prompt写作指南和5星博客等资源梳理
- Prompt和LLM论文细分方向梳理
- 解密Prompt系列1. Tunning-Free Prompt：GPT2 & GPT3 & LAMA & AutoPrompt
- 解密Prompt系列2. 冻结Prompt微调LM： T5 & PET & LM-BFF
- 解密Prompt系列3. 冻结LM微调Prompt: Prefix-tuning & Prompt-tuning & P-tuning
- 解密Prompt系列4. 升级Instruction Tuning：Flan/T0/InstructGPT/TKInstruct
- 解密prompt系列5. APE+SELF=自动化指令集构建代码实现
- 解密Prompt系列6. lora指令微调扣细节-请冷静,1个小时真不够~
- 解密Prompt系列7. 偏好对齐RLHF-OpenAI·DeepMind·Anthropic对比分析
- 解密Prompt系列8. 无需训练让LLM支持超长输入:知识库 & Unlimiformer & PCW & NBCE
- 解密Prompt系列9. 模型复杂推理-思维链基础和进阶玩法
- 解密Prompt系列10. 思维链COT原理探究
- 解密Prompt系列11. 小模型也能COT，先天不足后天补
- 解密Prompt系列12. LLM Agent零微调范式 ReAct & Self Ask
- 解密Prompt系列13. LLM Agent指令微调方案: Toolformer & Gorilla
- 解密Prompt系列14. LLM Agent之搜索应用设计：WebGPT & WebGLM & WebCPM
- 解密Prompt系列15. LLM Agent之数据库应用设计：DIN & C3 & SQL-Palm & BIRD
- 解密Prompt系列16. LLM对齐经验之数据越少越好？LTD & LIMA & AlpaGasus
- 解密Prompt系列17. LLM对齐方案再升级 WizardLM & BackTranslation & SELF-ALIGN
大模型评估尚未出现北极星指标，榜单排名往往和实际使用能力存在较大差异，几天没看感觉有的榜单快被玩坏了......
| 榜单 | 结果 |
|---|---|
| AlpacaEval：LLM-based automatic evaluation | 开源模型王者vicuna,openchat, wizardlm |
| Huggingface Open LLM Leaderboard | MMLU只评估开源模型，Falcon夺冠，在Eleuther AI4个评估集上评估的LLM模型榜单,vicuna夺冠 |
| Berkley出品大模型排位赛榜有准中文榜单 | Elo评分机制，GPT4自然是稳居第一，GPT4>Claude>GPT3.5>Vicuna>others |
| CMU开源聊天机器人评测应用 | ChatGPT>Vicuna>others；在对话场景中训练可能很重要 |
| Z-Bench中文真格基金评测 | 国产中文模型的编程可用性还相对较低，大家水平差不太多，两版ChatGLM提升明显 |
| Chain-of-thought评估 | GSM8k, MATH等复杂问题排行榜 |
| InfoQ 大模型综合能力评估 | 面向中文，ChatGPT>文心一言> Claude>星火 |
| ToolBench: 工具调用评估榜单 | 工具微调模型和ChatGPT进行对比，提供评测脚本 |
| AgentBench: 推理决策评估榜单 | 清华联合多高校推出不同任务环境，例如购物，家居，操作系统等场景下模型推理决策能力 |
| FlagEval | 智源出品主观+客观LLM评分榜单 |
| Bird-Bench | 更贴合真实世界应用的超大数据库，需要领域知识的NL2SQL榜单，模型追赶人类尚有时日 |
| kola | 以世界知识为核心的评价基准，包括已知的百科知识和未知的近90天网络发布内容，评价知识记忆，理解，应用和创造能力 |
| CEVAL | 中文知识评估，覆盖52个学科，机器评价主要为多项选择 |
| CMMLU | 67个主题中文知识和推理能力评估，多项选择机器评估 |
| 模型链接 | 模型描述 |
|---|---|
| LLama2 | Open Meta带着可商用开源的羊驼2模型来了~ |
| 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 |
| WizardLM | 微软新发布13B，登顶AlpacaEval开源模型Top3，使用ChatGPT对指令进行复杂度进化微调LLama2 |
| OpenChat | 80k ShareGPT对话微调LLama-2 13B开源模型中的战斗机 |
| Guanaco | LLama 7B基座，在alpaca52K数据上加入534K多语言指令数据微调 |
| LLaMA | Meta开源指令微调LLM，规模70 亿到 650 亿不等 |
| MPT | MosaicML开源的预训练+指令微调的新模型，可商用，支持84k tokens超长输入 |
| Falcon | Falcon由阿联酋技术研究所在超高质量1万亿Token上训练得到1B，7B，40B开源，免费商用！土豪们表示钱什么的格局小了 |
| RedPajama | RedPajama项目既开源预训练数据后开源3B，7B的预训练+指令微调模型 |
| koala | 使用alpaca，HC3等开源指令集+ ShareGPT等ChatGPT数据微调llama，在榜单上排名较高 |
| 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 |
| Dromedary | IBM self-aligned model with the LLaMA base |
| ColossalChat | HPC-AI Tech开源的Llama+RLHF微调 |
| 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指令微调开源模型在GPT-J-6B上微调 |
| OpenChatKit | openai研究员打造GPT-NoX-20B微调+6B审核模型过滤 |
| 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 |
| EXLLama | Python/C++/CUDA implementation of Llama for use with 4-bit GPTQ weight |
| LongChat | llama-13b使用condensing rotary embedding technique微调的长文本模型 |
| MPT-30B | MosaicML开源的在8Ktoken上训练的大模型 |
| 模型链接 | 模型描述 |
|---|---|
| ChatGLM2 | 32K长文本，FlashAttention+Multi-Query Attenion的显存优化，更强推理能力，哈哈不过很多简单问题也硬要COT，中英平行能力似乎略有下降的ChatGLM2，但是免费商用！ |
| 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。chatglm2支持超长文本，可免费商用啦！ |
| LLama2-chinese | 没等太久中文预训练微调后的llama2它来了~ |
| YuLan-chat2 | 高瓴人工智能基于Llama-2中英双语继续预训练+指令微调/对话微调 |
| ziya | IDEA研究院在7B/13B llama上继续预训练+SFT+RM+PPO+HFTT+COHFT+RBRS |
| Baichuan | 百川智能开源7B大模型可商用免费 |
| Baichuan2 | 百川第二代，提供了7B/13B Base和chat的版本 |
| XWin-LM | llama2 + SFT + RLHF |
| Chinese-LLaMA-Alpaca | 哈工大中文指令微调的LLaMA |
| Moss | 为复旦正名！开源了预训练，指令微调的全部数据和模型。可商用 |
| Qwen-7B | 阿里开源，可商用，通义千文7B模型 |
| IntenrLM | 书生浦语在过万亿 token 数据上训练的多语千亿参数基座模型 |
| Aquila2 | 智源更新Aquila2模型系列包括全新34B |
| Aquila | 智源开源7B大模型可商用免费 |
| kimi Chat | Moonshot上线超长文本LLM 可输入20W上文需要申请试用 |
| PandaLLM | LLAMA2上中文wiki继续预训练+COIG指令微调 |
| XVERSE | 据说中文超越llama2的元象开源模型13B模型 |
| BiLLa | LLama词表扩充预训练+预训练和任务1比1混合SFT+指令样本SFT三阶段训练 |
| Phoenix | 港中文开源凤凰和奇美拉LLM，Bloom基座，40+语言支持 |
| Wombat-7B | 达摩院开源无需强化学习使用RRHF对齐的语言模型, alpaca基座 |
| TigerBot | 虎博开源了7B 180B的模型以及预训练和微调语料 |
| Luotuo | 中文指令微调的LLaMA，和ChatGLM |
| OpenBuddy | Llama 多语言对话微调模型 |
| Chinese Vincuna | LLama 7B基座，使用Belle+Guanaco数据训练 |
| Linly | Llama 7B基座，使用belle+guanaco+pclue+firefly+CSL+newscommentary等7个指令微调数据集训练 |
| Firefly | 中文2.6B模型，提升模型中文写作，古文能力，待开源全部训练代码，当前只有模型 |
| 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 |
| Chatyuan | chatgpt出来后最早的国内开源对话模型，T5架构是下面PromptCLUE的衍生模型 |
| 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 |
| BayLing | 基于LLama7B/13B，增强的语言对齐的英语/中文大语言模型 |
| 领域 | 模型链接 | 模型描述 |
|---|---|---|
| 医疗 | MedGPT | 医联发布的 |
| 医疗 | MedPalm | Google在Faln-PaLM的基础上通过多种类型的医疗QA数据进行prompt-tuning指令微调得到，同时构建了MultiMedQA |
| 医疗 | ChatDoctor | 110K真实医患对话样本+5KChatGPT生成数据进行指令微调 |
| 医疗 | Huatuo Med-ChatGLM | 医学知识图谱和chatgpt构建中文医学指令数据集+医学文献和chatgpt构建多轮问答数据 |
| 医疗 | Chinese-vicuna-med | Chinese-vicuna在cMedQA2数据上微调 |
| 医疗 | OpenBioMed | 清华AIR开源轻量版BioMedGPT, 知识图谱&20+生物研究领域多模态预训练模型 |
| 医疗 | DoctorGLM | ChatDoctor+MedDialog+CMD 多轮对话+单轮指令样本微调GLM |
| 医疗 | MedicalGPT-zh | 自建的医学数据库ChatGPT生成QA+16个情境下SELF构建情景对话 |
| 医疗 | PMC-LLaMA | 医疗论文微调Llama |
| 医疗 | PULSE | Bloom微调+继续预训练 |
| 医疗 | NHS-LLM | Chatgpt生成的医疗问答，对话，微调模型 |
| 医疗 | 神农医疗大模型 | 以中医知识图谱的实体为中心生成的中医知识指令数据集11w+，微调LLama-7B |
| 医疗 | 岐黄问道大模型 | 3个子模型构成，已确诊疾病的临床治疗模型+基于症状的临床诊疗模型+中医养生条理模型，看起来是要ToB落地 |
| 医疗 | Zhongjing | 基于Ziya-LLama+医疗预训练+SFT+RLHF的中文医学大模型 |
| 医疗 | MeChat | 心理咨询领域，通过chatgpt改写多轮对话56k |
| 医疗 | SoulChat | 心理咨询领域中文长文本指令与多轮共情对话数据联合指令微调 ChatGLM-6B |
| 医疗 | MindChat | MindChat-Baichuan-13B,Qwen-7B,MindChat-InternLM-7B使用不同基座在模型安全，共情，人类价值观对其上进行了强化 |
| 医疗 | DISC-MedLLM | 疾病知识图谱构建QA对+QA对转化成单论对话+真实世界数据重构+人类偏好数据筛选，SFT微调baichuan |
| 法律 | LawGPT-zh | 利用ChatGPT清洗CrimeKgAssitant数据集得到52k单轮问答+我们根据中华人民共和国法律手册上最核心的9k法律条文，利用ChatGPT联想生成具体的情景问答+知识问答使用ChatGPT基于文本构建QA对 |
| 法律 | LawGPT | 基于llama+扩充词表二次预训练+基于法律条款构建QA指令微调 |
| 法律 | Lawyer Llama | 法律指令微调数据集：咨询+法律考试+对话进行指令微调 |
| 法律 | LexiLaw | 法律指令微调数据集：问答+书籍概念解释，法条内容进行指令微调 |
| 法律 | ChatLaw | 北大推出的法律大模型，应用形式很新颖类似频道内流一切功能皆融合在对话形式内 |
| 法律 | 录问模型 | 在baichuan基础上40G二次预训练+100K指令微调，在知识库构建上采用了Emb+意图+关键词联想结合的方案 |
| 金融 | FinChat.io | 使用最新的财务数据，电话会议记录，季度和年度报告，投资书籍等进行训练 |
| 金融 | OpenGPT | 领域LLM指令样本生成+微调框架 |
| 金融 | 乾元BigBang金融2亿模型 | 金融领域预训练+任务微调 |
| 金融 | 度小满千亿金融大模型 | 在Bloom-176B的基础上进行金融+中文预训练和微调 |
| 金融 | bondGPT | GPT4在细分债券市场的应用开放申请中 |
| 金融 | IndexGPT | JPMorgan在研的生成式投资顾问 |
| 金融 | 恒生LightGPT | 金融领域继续预训练+插件化设计 |
| 金融 | 知彼阿尔法 | 企查查商查大模型 |
| 金融 | AlphaBox | 熵简科技发布大模型金融应用，多文档问答+会议转录+文档编辑 |
| 金融 | 曹植 | 达观发布金融大模型融合data2text等金融任务，赋能报告写作 |
| 金融 | 聚宝盆 | 基于 LLaMA 系基模型经过中文金融知识指令精调/指令微调(Instruct-tuning) 的微调模型 |
| 金融 | PIXIU | 整理了多个金融任务数据集加入了时间序列数据进行指令微调 |
| 金融 | ChatFund | 韭圈儿发布的第一个基金大模型，看起来是做了多任务指令微调，和APP已有的数据功能进行了全方位的打通，从选基，到持仓分析等等 |
| 金融 | FinGPT | 金融传统任务微调 or chatgpt生成金融工具调用 |
| 金融 | CFGPT | 金融预训练+指令微调+RAG等检索任务增强 |
| 编程 | Starcoder | 80种编程语言+Issue+Commit训练得到的编程大模型 |
| 编程 | ChatSQL | 基于ChatGLM实现NL2sql |
| 编程 | codegeex | 13B预训练+微调多语言变成大模型 |
| 编程 | codegeex2 | Chatglm2的基础上CodeGeeX2-6B 进一步经过了 600B 代码数据预训练 |
| 编程 | stabelcode | 560B token多语言预训练+ 120,000 个 Alpaca指令对齐 |
| 编程 | SQLCoder | 在StarCoder的基础上微调15B超越gpt3.5 |
| 数学 | MathGPT | 是好未来自主研发的，面向全球数学爱好者和科研机构，以解题和讲题算法为核心的大模型。 |
| 数学 | MammoTH | 通过COT+POT构建了MathInstruct数据集微调llama在OOD数据集上超越了WizardLM |
| 交通 | TransGPT | LLama-7B+34.6万领域预训练+5.8万条领域指令对话微调（来自文档问答） |
| 交通 | TrafficGPT | ChatGPT+Prompt实现规划，调用交通流量领域专业TFM模型，TFM负责数据分析，任务执行，可视化等操作 |
| 科技 | Mozi | 红睡衣预训练+论文QA数据集 + ChatGPT扩充科研对话数据 |
| 天文 | StarGLM | 天文知识指令微调，项目进行中后期考虑天文二次预训练+KG |
| 写作 | 阅文-网文大模型介绍 | 签约作者内测中，主打的内容为打斗场景，剧情切换，环境描写，人设，世界观等辅助片段的生成 |
| 写作 | MediaGPT | LLama-7B扩充词表+指令微调，指令来自国内媒体专家给出的在新闻创作上的80个子任务 |
| 电商 | EcomGPT | 电商领域任务指令微调大模型，指令样本250万，基座模型是Bloomz |
| 工具描述 | 链接 |
|---|---|
| FlexFlow：模型部署推理框架 | https://github.com/flexflow/FlexFlow |
| Medusa：针对采样解码的推理加速框架，可以和其他策略结合 | https://github.com/FasterDecoding/Medusa |
| FlexGen: LLM推理 CPU Offload计算架构 | https://github.com/FMInference/FlexGen |
| VLLM：超高速推理框架Vicuna，Arena背后的无名英雄，比HF快24倍，支持很多基座模型 | https://github.com/vllm-project/vllm |
| Streamingllm: 新注意力池Attention方案，无需微调拓展模型推理长度，同时为推理提速 | https://github.com/mit-han-lab/streaming-llm |
- NexusGPT:
: AutoGPT可以出来工作了，第一个全AI Freelance平台
- 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：可以进行人为每一步交互的的AutoGPT
- agentgpt: 基础版AutoGPT
- do Anything: AutoGPT Like的to Do List生成器
- ChatMind: chatgpt生成思维导图，模板很丰富，泛化性也不错，已经被XMind收购了~
⭐
- 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
- AutoLabel: AutoLabel标注方案
- 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！
- 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
- 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
- sql translate: text2sql，利用 OpenAI 的 API 实现的一个很简单的工具，sql到文字，文字到sql
- ai2sql: text2sql老牌公司，相比sqltranslate功能更全面，支持SQL 语法检查、格式化和生成公式
- chat2query: text2sql 相比以上两位支持更自然的文本指令，以及更复杂的数据分析类的sql生成
⭐
- OuterBase: text2sql 设计风格很吸睛！电子表格结合mysql和dashboard，更适合数据分析宝宝
- 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
- storybird: 根据提示词生成故事绘本，还可以售卖
- OpenAI Cookbook: 提供OpenAI模型使用示例 ⭐
- OpenAI 接口被墙解决办法: 使用腾讯云搭建代理，亲测非常好用且手残党也可以轻松上手
- PromptPerfect:用魔法打败魔法，输入原始提示词，模型进行定向优化，试用后我有点沉默了，可以定向支持不同使用prompt的模型如Difussion，ChatGPT， Dalle等
- ClickPrompt: 为各种prompt加持的工具生成指令包括Difussion，chatgptdeng, 需要OpenAI Key
- ChatGPT ShortCut：提供各式场景下的Prompt范例，范例很全，使用后可以点赞！ ⭐
- Full ChatGPT Prompts + Resources: 各种尝尽的prompt范例，和以上场景有所不同
- learning Prompt: prompt engineering超全教程，和落地应用收藏，包括很多LLM调用Agent的高级场景 ⭐
- The art of asking chatgpt for high quality answers: 如何写Prompt指令出书了，链接是中文翻译的版本，比较偏基础使用
- Prompt-Engineer-Guide: 同learnig prompt类的集成教程，互相引用可还行？！分类索引做的更好些 ⭐
- OpenAI 应用汇总指南: 纯应用类的汇总指南
- AI 导航: 包括但不限于ChatGPT的应用汇总网站，更新很快，发现了一些新大陆
- AI Alignment Forum: RLHF等对齐相关最新论文和观点的讨论论坛
- Langchain: Chat with your data:吴恩达LLM实践课程
- 构筑大语言模型应用：应用开发与架构设计: 一本关于 LLM 在真实世界应用的开源电子书
- Large Language Models: Application through Production: 大模型应用Edx出品的课程
- OpenAI ChatGPT Intro
- OpenAI InstructGPT intro
- AllenAI ChatGPT能力解读：How does GPT Obtain its Ability? Tracing Emergent Abilities of Language Models to their Sources ⭐
- Huggingface ChatGPT能力解读：The techniques behind ChatGPT: RLHF, IFT, CoT, Red teaming, and more
- Stephen Wolfram ChatGPT能力解读: What Is ChatGPT Doing and Why Does It Work?
- Chatgpt相关解读汇总
- 麻省理工科技采访OpenAI工程师
- AGI历史与现状
- 张俊林 通向AGI之路：大型语言模型（LLM）技术精要
- 知乎回答 OpenAI 发布 GPT-4，有哪些技术上的优化或突破?
- 追赶ChatGPT的难点与平替
- 压缩即泛化，泛化即智能 ⭐
- 陆奇最新演讲实录：我的大模型世界观｜第十四期
- LLM Powered Autonomous Agents ⭐
- All You Need to Know to Build Your First LLM App ⭐
- GPT-4 Architecture, Infrastructure, Training Dataset, Costs, Vision, MoE
- 为什么伟大不能被计划: OpenAI研究员出书
- 拾象投研机构对LLM的调研报告（文中有两次PPT的申请链接）: 对大模型应用给出了很全面的总结梳理
- 启明创投State of Generative AI 2023: 最近发现应用落地才是LLM真正产生价值的核心，开始更多关注一些投研的分析报告
- How to Use AI to Do Stuff: An Opinionated Guide
- Llama 2: an incredible open LLM
- Wolfram语言之父新书：这就是ChatGPT
- 谷歌出品：对大模型领悟能力的一些探索很有意思 Do Machine Learning Models Memorize or Generalize? ⭐
- OpenAI首席科学家最新讲座解读LM无监督预训练学了啥 An observation on Generalization ⭐
- The Complete Beginners Guide To Autonomous Agents: Octane AI创始人 Matt Schlicht发表的关于人工智能代理的一些思考
- An Initial Exploration of Theoretical Support for Language Model Data Engineering. Part 1: Pretraining: 符尧大佬系列新作，通过了解大模型背后的数据工程来了解模型本质，第一篇预训练数据
- https://github.com/dongguanting/In-Context-Learning_PaperList
- https://github.com/thunlp/PromptPapers
- https://github.com/Timothyxxx/Chain-of-ThoughtsPapers
- https://github.com/thunlp/ToolLearningPapers
- https://github.com/MLGroupJLU/LLM-eval-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
- What Language Model Architecture and Pretraining objects work best for zero shot generalization ⭐
- Towards Reasoning in Large Language Models: A Survey
- Reasoning with Language Model Prompting: A Survey ⭐
- An Overview on Language Models: Recent Developments and Outlook ⭐
- A Survey of Large Language Models[6.29更新版]
- Unifying Large Language Models and Knowledge Graphs: A Roadmap
- Augmented Language Models: a Survey ⭐
- Domain Specialization as the Key to Make Large Language Models Disruptive: A Comprehensive Survey
- Challenges and Applications of Large Language Models
- The Rise and Potential of Large Language Model Based Agents: A Survey
- In Context Learning
- LARGER LANGUAGE MODELS DO IN-CONTEXT LEARNING DIFFERENTLY
- 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 ⭐
- Rethinking the Role of Demonstrations What Makes incontext learning work? ⭐
- Trained Transformers Learn Linear Models In-Context
- 涌现能力
- Sparks of Artificial General Intelligence: Early experiments with GPT-4
- Emerging Ability of Large Language Models ⭐
- LANGUAGE MODELS REPRESENT SPACE AND TIME
- 能力评估
- IS CHATGPT A GENERAL-PURPOSE NATURAL LANGUAGE PROCESSING TASK SOLVER?
- Can Large Language Models Infer Causation from Correlation?
- Holistic Evaluation of Language Model
- Harnessing the Power of LLMs in Practice: A Survey on ChatGPT and Beyond
- Theory of Mind May Have Spontaneously Emerged in Large Language Models
- Beyond The Imitation Game: Quantifying And Extrapolating The Capabilities Of Language Models
- Do Models Explain Themselves? Counterfactual Simulatability of Natural Language Explanations
- Demystifying GPT Self-Repair for Code Generation
- Evidence of Meaning in Language Models Trained on Programs
- Can Explanations Be Useful for Calibrating Black Box Models
- On the Robustness of ChatGPT: An Adversarial and Out-of-distribution Perspective
- Language acquisition: do children and language models follow similar learning stages?
- 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
- GLM-130B: AN OPEN BILINGUAL PRE-TRAINED MODEL
- LLaMA: Open and Efficient Foundation Language Models
- PaLM: Scaling Language Modeling with Pathways
- PaLM 2 Technical Report
- GPT-4 Technical Report
- Backpack Language Models
- Llama 2: Open Foundation and Fine-Tuned Chat Models
- OpenBA: An Open-sourced 15B Bilingual Asymmetric seq2seq Model Pre-trained from Scratch
- Sheared LLaMA: Accelerating Language Model Pre-training via Structured Pruning
- 经典方案
- Flan: FINETUNED LANGUAGE MODELS ARE ZERO-SHOT LEARNERS ⭐
- Flan-T5: Scaling Instruction-Finetuned Language Models
- ExT5: Towards Extreme Multi-Task Scaling for Transfer Learning
- Instruct-GPT: Training language models to follow instructions with human feedback ⭐
- 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
- ZeroPrompt: Scaling Prompt-Based Pretraining to 1,000 Tasks Improves Zero-shot Generalization
- Unnatural Instructions: Tuning Language Models with (Almost) No Human Labor
- INSTRUCTEVAL Towards Holistic Evaluation of Instrucion-Tuned Large Language Models
- 更少，质量更高、更多样的指令数据带来质变
- LIMA: Less Is More for Alignment ⭐
- Maybe Only 0.5% Data is Needed: A Preliminary Exploration of Low Training Data Instruction Tuning
- Textbooks Are All You Need ⭐
- AlpaGasus: Training A Better Alpaca with Fewer Data
- InstructionGPT-4: A 200-Instruction Paradigm for Fine-Tuning MiniGPT-4
- Instruction Mining: High-Quality Instruction Data Selection for Large Language Models
- Visual Instruction Tuning with Polite Flamingo
- 新对齐/微调方案
- WizardLM: Empowering Large Language Models to Follow Complex Instructions
- Becoming self-instruct: introducing early stopping criteria for minimal instruct tuning
- Self-Alignment with Instruction Backtranslation ⭐
- Mixture-of-Experts Meets Instruction Tuning:A Winning Combination for Large Language Models
- Goat: Fine-tuned LLaMA Outperforms GPT-4 on Arithmetic Tasks
- PROMPT2MODEL: Generating Deployable Models from Natural Language Instructions
- OpinionGPT: Modelling Explicit Biases in Instruction-Tuned LLMs
- Principle-Driven Self-Alignment of Language Models from Scratch with Minimal Human Supervision
- Improving Language Model Negotiation with Self-Play and In-Context Learning from AI Feedback
- 微调经验/实验报告
- 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
- A Comparative Study between Full-Parameter and LoRA-based Fine-Tuning on Chinese Instruction Data for Large LM
- Exploring ChatGPT’s Ability to Rank Content: A Preliminary Study on Consistency with Human Preferences
- Towards Better Instruction Following Language Models for Chinese: Investigating the Impact of Training Data and Evaluation
- LaMDA: Language Models for Dialog Applications
- Sparrow: Improving alignment of dialogue agents via targeted human judgements ⭐
- 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
- DialogStudio: Towards Richest and Most Diverse Unified Dataset Collection for Conversational AI
- Enhancing Chat Language Models by Scaling High-quality Instructional Conversations
- DiagGPT: An LLM-based Chatbot with Automatic Topic Management for Task-Oriented Dialogue
- 基础&进阶用法
- [zero-shot-COT] Large Language Models are Zero-Shot Reasoners ⭐
- [few-shot COT] Chain of Thought Prompting Elicits Reasoning in Large Language Models ⭐
- SELF-CONSISTENCY IMPROVES CHAIN OF THOUGHT REASONING IN LANGUAGE MODELS
- LEAST-TO-MOST PROMPTING ENABLES COMPLEX REASONING IN LARGE LANGUAGE MODELS ⭐
- Tree of Thoughts: Deliberate Problem Solving with Large Language Models
- Plan-and-Solve Prompting: Improving Zero-Shot Chain-of-Thought Reasoning by Large Language Models
- Decomposed Prompting A MODULAR APPROACH FOR Solving Complex Tasks
- Successive Prompting for Decomposing Complex Questions
- Verify-and-Edit: A Knowledge-Enhanced Chain-of-Thought Framework
- Beyond Chain-of-Thought, Effective Graph-of-Thought Reasoning in Large Language Models
- Tree-of-Mixed-Thought: Combining Fast and Slow Thinking for Multi-hop Visual Reasoning
- LAMBADA: Backward Chaining for Automated Reasoning in Natural Language
- Algorithm of Thoughts: Enhancing Exploration of Ideas in Large Language Models
- Graph of Thoughts: Solving Elaborate Problems with Large Language Models
- 分领域COT [Math, Code, Tabular, QA]
- Solving Quantitative Reasoning Problems with Language Models
- SHOW YOUR WORK: SCRATCHPADS FOR INTERMEDIATE COMPUTATION WITH LANGUAGE MODELS
- Solving math word problems with processand outcome-based feedback
- CodeRL: Mastering Code Generation through Pretrained Models and Deep Reinforcement Learning
- T-SciQ: Teaching Multimodal Chain-of-Thought Reasoning via Large Language Model Signals for Science Question Answering
- LEARNING PERFORMANCE-IMPROVING CODE EDITS
- Large Language Models are Versatile Decomposers: Decompose Evidence and Questions for Table-based Reasoning
- Tab-CoT: Zero-shot Tabular Chain of Thought
- 原理分析
- Towards Understanding Chain-of-Thought Prompting: An Empirical Study of What Matters ⭐
- TEXT AND PATTERNS: FOR EFFECTIVE CHAIN OF THOUGHT IT TAKES TWO TO TANGO
- Towards Revealing the Mystery behind Chain of Thought: a Theoretical Perspective
- Large Language Models Can Be Easily Distracted by Irrelevant Context
- 小模型COT蒸馏
- Specializing Smaller Language Models towards Multi-Step Reasoning ⭐
- Teaching Small Language Models to Reason
- Large Language Models are Reasoning Teachers
- Distilling Reasoning Capabilities into Smaller Language Models
- The CoT Collection: Improving Zero-shot and Few-shot Learning of Language Models via Chain-of-Thought Fine-Tuning
- COT样本自动构建/选择
- STaR: Self-Taught Reasoner Bootstrapping ReasoningWith Reasoning
- AutoCOT：AUTOMATIC CHAIN OF THOUGHT PROMPTING IN LARGE LANGUAGE MODELS
- Large Language Models Can Self-Improve
- Active Prompting with Chain-of-Thought for Large Language Models
- COMPLEXITY-BASED PROMPTING FOR MULTI-STEP REASONING
- others
- OlaGPT Empowering LLMs With Human-like Problem-Solving abilities
- Challenging BIG-Bench tasks and whether chain-of-thought can solve them
- Large Language Models are Better Reasoners with Self-Verification
- ThoughtSource A central hub for large language model reasoning data
- Two Failures of Self-Consistency in the Multi-Step Reasoning of LLMs
- 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 ⭐
- Pretraining Language Models with Human Preferences
- The Capacity for Moral Self-Correction in Large Language Models
- AllenAI, RL4LM：IS REINFORCEMENT LEARNING (NOT) FOR NATURAL LANGUAGE PROCESSING BENCHMARKS
- 改良方案
- RRHF: Rank Responses to Align Language Models with Human Feedback without tears
- PRM：Let's verify step by step
- Chain of Hindsight Aligns Language Models with Feedback
- AlpacaFarm: A Simulation Framework for Methods that Learn from Human Feedback
- Open Problems and Fundamental Limitations of Reinforcement Learning from Human Feedback
- RAFT: Reward rAnked FineTuning for Generative Foundation Model Alignment
- Training Socially Aligned Language Models in Simulated Human Society
- 基于prompt通用方案
- ReAct: SYNERGIZING REASONING AND ACTING IN LANGUAGE MODELS ⭐
- Self-ask: MEASURING AND NARROWING THE COMPOSITIONALITY GAP IN LANGUAGE MODELS ⭐
- MRKL SystemsA modular, neuro-symbolic architecture that combines large language models, external knowledge sources and discrete reasoning
- PAL: Program-aided Language Models
- ART: Automatic multi-step reasoning and tool-use for large language models
- ReWOO: Decoupling Reasoning from Observations for Efficient Augmented Language Models ⭐
- Interleaving Retrieval with Chain-of-Thought Reasoning for Knowledge-Intensive Multi-Step Questions
- Chameleon: Plug-and-Play Compositional Reasoning with Large Language Models ⭐
- Faithful Chain-of-Thought Reasoning
- Reflexion: Language Agents with Verbal Reinforcement Learning ⭐
- Search-in-the-Chain: Towards Accurate, Credible and Traceable Large Language Models for Knowledge-intensive Tasks
- Verify-and-Edit: A Knowledge-Enhanced Chain-of-Thought Framework
- RestGPT: Connecting Large Language Models with Real-World RESTful APIs
- ChatCoT: Tool-Augmented Chain-of-Thought Reasoning on Chat-based Large Language Models
- 基于微调通用方案
- TALM: Tool Augmented Language Models
- Toolformer: Language Models Can Teach Themselves to Use Tools ⭐
- Tool Learning with Foundation Models
- Tool Maker：Large Language Models as Tool Maker
- TaskMatrix.AI: Completing Tasks by Connecting Foundation Models with Millions of APIs
- 检索增强方案
- WebGPT：Browser-assisted question-answering with human feedback
- WebGLM: Towards An Efficient Web-Enhanced Question Answering System with Human Preferences
- WebCPM: Interactive Web Search for Chinese Long-form Question Answering ⭐
- REPLUG: Retrieval-Augmented Black-Box Language Models
- Query Rewriting for Retrieval-Augmented Large Language Models
- RETA-LLM: A Retrieval-Augmented Large Language Model Toolkit
- Atlas: Few-shot Learning with Retrieval Augmented Language Models
- RRAML: Reinforced Retrieval Augmented Machine Learning
- Investigating the Factual Knowledge Boundary of Large Language Models with Retrieval Augmentation
- PDFTriage: Question Answering over Long, Structured Documents
- 调用模型方案
- HuggingGPT: Solving AI Tasks with ChatGPT and its Friends in HuggingFace
- Gorilla：Large Language Model Connected with Massive APIs ⭐
- OpenAGI: When LLM Meets Domain Experts
- 垂直领域
- WebShop: Towards Scalable Real-World Web Interaction with Grounded Language Agents
- ToolkenGPT: Augmenting Frozen Language Models with Massive Tools via Tool Embeddings
- ChemCrow Augmenting large language models with chemistry tools
- Data-Copilot: Bridging Billions of Data and Humans with Autonomous Workflow
- GeneGPT: Augmenting Large Language Models with Domain Tools for Improved Access to Biomedical Information
- PointLLM: Empowering Large Language Models to Understand Point Clouds
- 评估
- Evaluating Verifiability in Generative Search Engines
- Mind2Web: Towards a Generalist Agent for the Web
- Auto-GPT for Online Decision Making: Benchmarks and Additional Opinions
- API-Bank: A Benchmark for Tool-Augmented LLMs
- ToolLLM: Facilitating Large Language Models to Master 16000+ Real-world APIs
- MultiAgent
- Generative Agents: Interactive Simulacra of Human Behavior
- AgentVerse: Facilitating Multi-Agent Collaboration and Exploring Emergent Behaviors in Agents
- CAMEL: Communicative Agents for "Mind" Exploration of Large Scale Language Model Society
- Exploring Large Language Models for Communication Games: An Empirical Study on Werewolf
- 其他
- LLM+P: Empowering Large Language Models with Optimal Planning Proficiency
- Inference with Reference: Lossless Acceleration of Large Language Models
- RecallM: An Architecture for Temporal Context Understanding and Question Answering
- 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
- SELF-QA Unsupervised Knowledge Guided alignment.
- GPT Self-Supervision for a Better Data Annotator
- The Flan Collection Designing Data and Methods
- Self-Consuming Generative Models Go MAD
- InstructEval: Systematic Evaluation of Instruction Selection Methods
- Overwriting Pretrained Bias with Finetuning Data
- WizardLM: Empowering Large Language Models to Follow Complex Instructions
- DoReMi: Optimizing Data Mixtures Speeds Up Language Model Pretraining
- The Pile: An 800GB Dataset of Diverse Text for Language Modeling
- CCNet: Extracting High Quality Monolingual Datasets fromWeb Crawl Data
- WanJuan: A Comprehensive Multimodal Dataset for Advancing English and Chinese Large Models
- CLUECorpus2020: A Large-scale Chinese Corpus for Pre-training Language Model
- MedGPT: Medical Concept Prediction from Clinical Narratives
- 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] ⭐
- Augmented Large Language Models with Parametric Knowledge Guiding
- XuanYuan 2.0: A Large Chinese Financial Chat Model with Hundreds of Billions Parameters
- ChatLaw Open-Source Legal Large Language Model ⭐
- MediaGPT : A Large Language Model For Chinese Media
- SMILE: Single-turn to Multi-turn Inclusive Language Expansion via ChatGPT for Mental Health Support
- KITLM: Domain-Specific Knowledge InTegration into Language Models for Question Answering
- FinVis-GPT: A Multimodal Large Language Model for Financial Chart Analysis
- EcomGPT: Instruction-tuning Large Language Models with Chain-of-Task Tasks for E-commerce
- FinGPT: Open-Source Financial Large Language Models
- TableGPT: Towards Unifying Tables, Nature Language and Commands into One GPT
- CFGPT: Chinese Financial Assistant with Large Language Model
- Zhongjing: Enhancing the Chinese Medical Capabilities of Large Language Model through Expert Feedback and Real-world Multi-turn Dialogue
- Parallel Context Windows for Large Language Models
- Structured Prompting: Scaling In-Context Learning to 1,000 Examples
- 苏剑林, NBCE：使用朴素贝叶斯扩展LLM的Context处理长度 ⭐
- Vcc: Scaling Transformers to 128K Tokens or More by Prioritizing Important Tokens
- Unlimiformer: Long-Range Transformers with Unlimited Length Input
- Scaling Transformer to 1M tokens and beyond with RMT
- RECURRENTGPT: Interactive Generation of (Arbitrarily) Long Text
- TRAIN SHORT, TEST LONG: ATTENTION WITH LINEAR BIASES ENABLES INPUT LENGTH EXTRAPOLATION ⭐
- FlashAttention: Fast and Memory-Efficient Exact Attention with IO-Awareness
- Extending Context Window of Large Language Models via Positional Interpolation
- LongNet: Scaling Transformers to 1,000,000,000 Tokens
- https://kaiokendev.github.io/til#extending-context-to-8k
- 苏剑林,Transformer升级之路：10、RoPE是一种β进制编码 ⭐
- 苏剑林,Transformer升级之路：11、将β进制位置进行到底
- 苏剑林,Transformer升级之路：12、无限外推的ReRoPE？
- Focused Transformer: Contrastive Training for Context Scaling
- Lost in the Middle: How Language Models Use Long Contexts ⭐
- EFFICIENT STREAMING LANGUAGE MODELS WITH ATTENTION SINKS
- Ring Attention with Blockwise Transformers for Near-Infinite Context
- 大模型方案
- DIN-SQL: Decomposed In-Context Learning of Text-to-SQL with Self-Correction ⭐
- C3: Zero-shot Text-to-SQL with ChatGPT ⭐
- SQL-PALM: IMPROVED LARGE LANGUAGE MODEL ADAPTATION FOR TEXT-TO-SQL
- BIRD Can LLM Already Serve as A Database Interface? A BIg Bench for Large-Scale Database Grounded Text-to-SQL ⭐
- A Case-Based Reasoning Framework for Adaptive Prompting in Cross-Domain Text-to-SQL
- ChatDB: AUGMENTING LLMS WITH DATABASES AS THEIR SYMBOLIC MEMORY
- A comprehensive evaluation of ChatGPT’s zero-shot Text-to-SQL capability
- Few-shot Text-to-SQL Translation using Structure and Content Prompt Learning
- Domain Knowledge Intensive
- Towards Knowledge-Intensive Text-to-SQL Semantic Parsing with Formulaic Knowledge
- Bridging the Generalization Gap in Text-to-SQL Parsing with Schema Expansion
- Towards Robustness of Text-to-SQL Models against Synonym Substitution
- FinQA: A Dataset of Numerical Reasoning over Financial Data
- others
- RESDSQL: Decoupling Schema Linking and Skeleton Parsing for Text-to-SQL
- MIGA: A Unified Multi-task Generation Framework for Conversational Text-to-SQL
- Survey of Hallucination in Natural Language Generation
- Trusting Your Evidence: Hallucinate Less with Context-aware Decoding ⭐
- SELF-REFINE:ITERATIVE REFINEMENT WITH SELF-FEEDBACK ⭐
- PROMPTING GPT-3 TO BE RELIABLE
- Enhancing Self-Consistency and Performance of Pre-Trained Language Models through Natural Language Inference
- On the Advance of Making Language Models Better Reasoners
- Progressive-Hint Prompting Improves Reasoning in Large Language Models
- ASK ME ANYTHING: A SIMPLE STRATEGY FOR PROMPTING LANGUAGE MODELS ⭐
- Inference-Time Intervention: Eliciting Truthful Answers from a Language Model
- Reflexion: an autonomous agent with dynamic memory and self-reflection
- Self-consistency for open-ended generations
- Check Your Facts and Try Again: Improving Large Language Models with External Knowledge and Automated Feedback
- Factuality Enhanced Language Models for Open-Ended Text Generation
- Adaptive Chameleon or Stubborn Sloth: Unraveling the Behavior of Large Language Models in Knowledge Clashes
- Rethinking with Retrieval: Faithful Large Language Model Inference
- RefGPT: Reference → Truthful & Customized Dialogues Generation by GPTs and for GPTs
- Enabling Large Language Models to Generate Text with Citations
- 事实性评估
- TRUSTWORTHY LLMS: A SURVEY AND GUIDELINE FOR EVALUATING LARGE LANGUAGE MODELS’ ALIGNMENT
- TrueTeacher: Learning Factual Consistency Evaluation with Large Language Models
- TRUE: Re-evaluating Factual Consistency Evaluation
- SelfCheckGPT: Zero-Resource Black-Box Hallucination Detection for Generative Large Language Models
- FACTSCORE: Fine-grained Atomic Evaluation of Factual Precision in Long Form Text Generation
- KoLA: Carefully Benchmarking World Knowledge of Large Language Models
- Fast Transformer Decoding: One Write-Head is All You Need
- Fast Inference from Transformers via Speculative Decoding
- GQA: Training Generalized Multi-Query Transformer Models from Multi-Head Checkpoints
- Skeleton-of-Thought: Large Language Models Can Do Parallel Decoding
- SkipDecode: Autoregressive Skip Decoding with Batching and Caching for Efficient LLM Inference
- BatchPrompt: Accomplish more with less
- ROME：Locating and Editing Factual Associations in GPT
- Transformer Feed-Forward Layers Are Key-Value Memories
- MEMIT: Mass-Editing Memory in a Transformer
- MEND：Fast Model Editing at Scale
- Editing Large Language Models: Problems, Methods, and Opportunities
- Calibrate Before Use: Improving Few-Shot Performance of Language Models
- In-Context Instruction Learning
- LEARNING PERFORMANCE-IMPROVING CODE EDITS
- Boosting Theory-of-Mind Performance in Large Language Models via Prompting
- Generated Knowledge Prompting for Commonsense Reasoning
- RECITATION-AUGMENTED LANGUAGE MODELS
- kNN PROMPTING: BEYOND-CONTEXT LEARNING WITH CALIBRATION-FREE NEAREST NEIGHBOR INFERENCE
- EmotionPrompt: Leveraging Psychology for Large Language Models Enhancement via Emotional Stimulus
- Causality-aware Concept Extraction based on Knowledge-guided Prompting
- LARGE LANGUAGE MODELS AS OPTIMIZERS
- InstructBLIP: Towards General-purpose Vision-Language Models with Instruction Tuning
- Visual ChatGPT: Talking, Drawing and Editing with Visual Foundation Models
- PaLM-E: An Embodied Multimodal Language Model
- LLava Visual Instruction Tuning
- MiniGPT-4: Enhancing Vision-Language Understanding with Advanced Large Language Models
- TabLLM: Few-shot Classification of Tabular Data with Large Language Models
- BLIVA: A Simple Multimodal LLM for Better Handling of Text-Rich Visual Questions
- mPLUG-Owl : Modularization Empowers Large Language Models with Multimodality
- LVLM eHub: A Comprehensive Evaluation Benchmark for Large VisionLanguage Models
- Pretraining on the Test Set Is All You Need 哈哈作者你是懂讽刺文学的
- Learnware: Small Models Do Big
- The economic potential of generative AI
- A PhD Student’s Perspective on Research in NLP in the Era of Very Large Language Models