DecryptPrompt

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

1. 国内外，垂直领域大模型
2. Agent和指令微调等训练框架
3. 开源指令，预训练，rlhf，对话，agent训练数据梳理
4. AIGC相关应用
5. prompt写作指南和5星博客等资源梳理
6. Prompt和LLM论文细分方向梳理

My blogs

• 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
• 解密Prompt系列18. LLM Agent之只有智能体的世界
• 解密Prompt系列19. LLM Agent之数据分析领域的应用：Data-Copilot & InsightPilot
• 解密Prompt系列20. LLM Agent 之再谈RAG的召回多样性优化
• 解密Prompt系列21. LLM Agent之再谈RAG的召回信息密度和质量
• ​解密Prompt系列22. LLM Agent之RAG的反思：放弃了压缩还是智能么？
• 解密Prompt系列23.大模型幻觉分类&归因&检测&缓解方案脑图全梳理
• 解密prompt系列24.RLHF新方案之训练策略：SLiC-HF & DPO & RRHF & RSO

LLMS

模型评测

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
https://opencompass.org.cn/	上海人工智能实验室推出的开源榜单
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
LLMEval3	复旦推出的知识问答榜单，涵盖大学作业和考题，题库尽可能来自非互联网避免模型作弊

国外开源模型

模型链接	模型描述
Gemma	谷歌商场开源模型2B，7B免费商用，开源第一易主了
Mixtral	法国“openai”开源基于MegaBlocks训练的MOE模型8*7B 32K
Mistral7B	法国“openai”开源Mistral，超过llama2当前最好7B模型
Dolphin-2.2.1-Mistral-7B	基于Mistral7B使用dolphin数据集微调
LLama2	Open Meta带着可商用开源的羊驼2模型来了~
LLaMA	Meta开源指令微调LLM，规模70 亿到 650 亿不等
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
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
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
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
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
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
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

国内开源模型

模型链接	模型描述
Baichuan2	百川第二代，提供了7B/13B Base和chat的版本
Baichuan	百川智能开源7B大模型可商用免费
ziya2	基于Llama2训练的ziya2它终于训练完了
ziya	IDEA研究院在7B/13B llama上继续预训练+SFT+RM+PPO+HFTT+COHFT+RBRS
Qwen1.5	通义千问升级1.5，支持32K上文
Qwen1-7B+14B+70B	阿里开源，可商用，通义千问7B,14B,70B Base和chat模型
InternLM2 7B+20B	商汤的书生模型2支持200K
Orion-14B-LongChat	猎户星空多语言模型支持320K
ChatGLM3	ChatGLM3发布，支持工具调用等更多功能，不过泛化性有待评估
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！
Yuan-2.0	浪潮发布Yuan2.0 2B，51B，102B
YI-200K	元一智能开源超长200K的6B，34B模型
YI	元一智能开源34B，6B模型
XVERSE-256K	元象发布13B免费商用大模型，虽然很长但是
XVERSE	元象发布13B免费商用大模型
DeepSeek-MOE	深度求索发布的DeepSeekMoE 16B Base和caht模型
DeepSeek	深度求索发布的7B，67B大模型
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
BlueLM	Vivo人工智能实验室开源大模型
zephyr-7B	HuggingFace 团队基于 UltraChat 和 UltraFeedback 训练了 Zephyr-7B 模型
XWin-LM	llama2 + SFT + RLHF
Skywork	昆仑万维集团·天工团队开源13B大模型可商用
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
InternLM	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
UltraLM系列	面壁智能开源UltraLM13B，奖励模型UltraRM，和批评模型UltraCM
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

国内外免费试用的大模型应用

模型链接	模型描述
PPLX-7B/70B	Perplexity.ai的Playground支持他们自家的PPLX模型和众多SOTA大模型，Gemma也支持了
kimi Chat	Moonshot超长文本LLM 可输入20W上文, 文档总结无敌
讯飞星火	科大讯飞
文心一言	百度
通义千问	阿里
百川	百川
ChatGLM	智谱轻言
DeepSeek	深度求索
360智脑	360
悟空	字节跳动

垂直领域模型&进展

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
金融	况客FOF智能投顾	基金大模型应用，基金投顾，支持nl2sql类的数据查询，和基金信息对比查询等
金融	DISC-FinLLM	复旦发布多微调模型组合金融系统，包括金融知识问答,金融NLP任务，金融计算，金融检索问答
金融	InvestLM	CFA考试，SEC， StackExchange投资问题等构建的金融指令微调LLaMA-65+
金融	HithinkGPT	同花顺发布金融大模型问财，覆盖查询，分析，对比，解读，预测等多个问题领域
金融	无涯Infinity	星环科技发布的金融大模型
金融	妙想	东方财富自研金融大模型开放试用
金融	DeepMoney	基于yi-34b-200k使用金融研报进行微调
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
数学	MetaMath	模型逆向思维解决数学问题，构建了新的MetaMathQA微调llama2
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
植物科学	PLLaMa	基于Llama使用植物科学领域学术论文继续预训练+sft扩展的领域模型
评估	Auto-J	上交开源了价值评估对齐13B模型
评估	JudgeLM	智源开源了 JudgeLM 的裁判模型，可以高效准确地评判各类大模型
评估	CritiqueLLM	智谱AI发布评分模型CritiqueLLM,支持含参考文本/无参考文本的评估打分

新时代RAG Embedding模型

模型链接	模型描述
Jina-Cobert	Jian AI开源中英德，8192 Token长文本Embedding
BGE-M3	智源开源多语言，稀疏+稠密表征，8192 Token长文本Embedding
BCE	网易开源更适配RAG任务的Embedding模型

Tool and Library

推理框架

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
llama2.c: llama2 纯C语言的推理框架	https://github.com/karpathy/llama2.c

指令微调，预训练，rlhf框架

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
RLLTE：港大，大疆等联合开源RLLTE开源学习框架	https://github.com/RLE-Foundation/rllte
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
北大开源河狸项目可复现RLHF，支持多数LLM，提供RLHF数据	https://github.com/PKU-Alignment/safe-rlhf
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
hugNLP:基于Huggingface开发继承Prompt技术，预训练和是指输入等多种方案	https://github.com/wjn1996/HugNLP
Deepspeed：针对RL训练和推理的整合优化	https://github.com/microsoft/DeepSpeed
Uerpy:预训练框架支持lm,mlm,unilm等	https://github.com/dbiir/UER-py
TecentPretrain: Uerpy的重构版本支持llama预训练	https://github.com/Tencent/TencentPretrain/tree/main
lamini: 整合指令数据生成，SFT，RLHF的工具库	https://github.com/lamini-ai/lamini/
Chain-of-thought-hub：模型推理能力评估平台	https://github.com/FranxYao/chain-of-thought-hub
EasyEdit：浙大开源支持多种模型，多种方案的模型知识精准编辑器	https://github.com/zjunlp/EasyEdit
OpenDelta：集成了各种增量微调方案的开源实现	https://github.com/thunlp/OpenDelta
Megablocks：MOE训练框架	https://github.com/stanford-futuredata/megablocks
Tutel：MOE训练框架	https://github.com/microsoft/tutel
TradingGym：参考openai gym的股票交易强化学习模拟器	https://github.com/astrologos/tradinggym
LongLora: 长文本微调框架	https://github.com/dvlab-research/LongLoRA

Auto/Multi Agent

工具描述	链接
AutoGen：微软开源多Agent顶层框架	https://github.com/microsoft/autogen
CrewAI: 比chatDev流程定义更灵活的多智能体框架	https://github.com/joaomdmoura/CrewAI
ChatDev: 面壁智能开源多智能体协作的虚拟软件公司	https://github.com/OpenBMB/ChatDev
Generative Agents:斯坦福AI小镇的开源代码	https://github.com/joonspk-research/generative_agents
BabyAGI：自执行LLM Agent	https://github.com/yoheinakajima/babyagi
AutoGPT：自执行LLM Agent	https://github.com/Torantulino/Auto-GPT
AutoGPT-Plugins：提供众多Auo-GPT官方和第三方的插件	https://github.com/Significant-Gravitas/Auto-GPT-Plugins
XAgent: 面壁智能开源双循环AutoGPT	https://github.com/OpenBMB/XAgent
MetaGPT: 覆盖软件公司全生命流程，例如产品经理等各个职业的AutoGPT	https://github.com/geekan/MetaGPT
ResearchGPT: 论文写作领域的AutoGPT，融合论文拆解+网络爬虫	https://github.com/assafelovic/gpt-researcher
MiniAGI：自执行LLM Agent	https://github.com/muellerberndt/mini-agi
AL Legion： 自执行LLM Agent	https://github.com/eumemic/ai-legion
AgentVerse：多模型交互环境	https://github.com/OpenBMB/AgentVerse
AgentSims: 给定一个社会环境，评估LLM作为智能体的预定任务目标完成能力的沙盒环境	https://github.com/py499372727/AgentSims/
GPTRPG：RPG环境 AI Agent游戏化	https://github.com/dzoba/gptrpg
GPTeam：多智能体交互	https://github.com/101dotxyz/GPTeam
GPTEngineer：自动工具构建和代码生成	https://github.com/AntonOsika/gpt-engineer
WorkGPT：类似AutoGPT	https://github.com/team-openpm/workgpt
AI-Town: 虚拟世界模拟器	https://github.com/a16z-infra/ai-town
webarena:网络拟真环境，可用于自主智能体的测试，支持在线购物，论坛，代码仓库etc	https://github.com/web-arena-x/webarena
MiniWoB++：100+web交互操作的拟真环境	https://github.com/Farama-Foundation/miniwob-plusplus

Agent工具框架类

工具描述	链接
OpenAgents: 开源版ChatGPT-Plus搭建框架	https://github.com/xlang-ai/OpenAgents
langchain：LLM Agent框架	https://github.com/hwchase17/langchain
llama index：LLM Agent框架	https://github.com/jerryjliu/llama_index
Langroid: LLM Agent框架	https://github.com/langroid/langroid
Ragas: 评估检索增强LLM效果的框架，基于大模型prompt评估事实性，召回相关性，召回内容质量，回答相关性等	https://github.com/explodinggradients/ragas#fire-quickstart
fastRAG：检索框架，包括多索引检索，KG构建等基础功能	https://github.com/IntelLabs/fastRAG/tree/main
langflow：把langchain等agent组件做成了可拖拽式的UI	https://github.com/logspace-ai/langflow
PhiData：把工具调用抽象成function call的Agent框架	https://github.com/phidatahq/phidata
Haystack: LLM Agent 框架，pipeline的设计模式个人感觉比langchain更灵活更简洁	https://github.com/deepset-ai/haystack
EdgeChain: 通过Jsonnet配置文件实现LLM Agent	https://github.com/arakoodev/EdgeChains/tree/main
semantic-kernel：整合大模型和编程语言的SDK	https://github.com/microsoft/semantic-kernel
BMTTools: 清华出品多工具调用开源库，提供微调数据和评估ToolBench	https://github.com/OpenBMB/BMTools
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
LLM-ToolMaker:让LLM自己制造Agent	https://github.com/ctlllll/LLM-ToolMaker
Gorilla: LLM调用大量API	https://github.com/ShishirPatil/gorilla
wenda:闻达小模型整合搜索用于知识融入	https://github.com/l15y/wenda
Alexandria: 从Arix论文开始把整个互联网变成向量索引，可以免费下载	https://alex.macrocosm.so/download
RapidAPI: 统一这个世界的所有API，最大API Hub，有调用成功率，latency等，是真爱！	https://rapidapi.com/hub
Open-Interpreter：命令行聊天框架	https://github.com/KillianLucas/open-interpreter
AnythingLLM: langchain推出的支持本地部署开源模型的框架	https://github.com/Mintplex-Labs/anything-llm
PromptFlow：微软推出的大模型应用框架	https://github.com/microsoft/promptflow
Coze:字节跳动推出的个性化Agent定制应用支持多个大模型丰富插件集使用	https://www.coze.com/username?redirect=/explore

其他垂直领域Agent

工具描述	链接
Deep-KE：基于LLM对数据进行智能解析实现知识抽取	https://github.com/zjunlp/DeepKE
IncarnaMind：多文档RAG方案，动态chunking的方案可以借鉴	https://github.com/junruxiong/IncarnaMind
Vectra：平台化的LLM Agent搭建方案，从索引构建，内容召回排序，到事实检查的LLM生成	https://vectara.com/tour-vectara/
Data-Copilot：时间序列等结构化数据分析领域的Agent解决方案	https://github.com/zwq2018/Data-Copilot
DB-GPT: 以数据库为基础的GPT实验项目，使用本地化的GPT大模型与您的数据和环境进行交互	https://db-gpt.readthedocs.io/projects/db-gpt-docs-zh-cn/zh_CN/latest/index.html
guardrails：降低模型幻觉的python框架，promp模板+validation+修正	https://github.com/shreyar/guardrails
guidance：微软新开源框架，同样是降低模型幻觉的框架，prompt+chain的升级版加入逐步生成和思维链路	https://github.com/guidance-ai/guidance
SolidGPT: 上传个人数据，通过命令交互创建项目PRD等	https://github.com/AI-Citizen/SolidGPT
HR-Agent: 类似HR和员工交互，支持多工具调用	https://github.com/stepanogil/autonomous-hr-chatbot
BambooAI：数据分析Agent	https://github.com/pgalko/BambooAI
AlphaCodium：通过Flow Engineering完成代码任务	https://github.com/Codium-ai/AlphaCodium

Training Data

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	GPT4-for-LLM 中文+英文+对比指令	https://github.com/Instruction-Tuning-with-GPT-4/GPT-4-LLM
指令微调	GPTTeacher更多样的通用指令，角色扮演和代码指令	https://github.com/teknium1/GPTeacher/tree/main
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	alpaca指令GPT4生成，和以上几版对比显著质量更高，回复更长	https://github.com/Instruction-Tuning-with-GPT-4/GPT-4-LLM/tree/main
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，还有补充的QA生成样本	https://huggingface.co/datasets/JosephusCheung/GuanacoDataset
指令微调	OIG中文指令包括翻译alpaca+natural+unnatural，多轮对话，考试，leetcode指令	https://github.com/BAAI-Zlab/COIG
指令微调	Vicuna训练使用的样本，用API获取了sharegpt上用户和chatgpt对话历史，部分网友整理到了HF	https://github.com/domeccleston/sharegpt https://huggingface.co/datasets/anon8231489123/ShareGPT_Vicuna_unfiltered/tree/main
指令微调	HC3指令数据中英文，包括金融，开放QA，百科，DBQA，医学等包含人工回复	https://huggingface.co/datasets/Hello-SimpleAI/HC3-Chinese/tree/main
指令微调	MOSS开源的SFT数据包含使用plugin的对话数据	https://huggingface.co/datasets/Hello-SimpleAI/HC3-Chinese/tree/main
指令微调	InstructWild数据：用四处爬取的chatgpt指令作为种子self-instruct扩充生成，中英双语	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，有数学，多轮对话，校色对话等等	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	alpaca COT对多个数据源进行了清理并统一格式放到的了HF, 重点是人工整理的COT数据	https://github.com/PhoebusSi/Alpaca-CoT
指令微调	人工编写包含23种常见的中文NLP任务的指令数据，中文写作方向	https://github.com/yangjianxin1/Firefly
指令微调	Amazon COT指令样本包括各类QA，bigbench，math等	https://github.com/amazon-science/auto-cot
指令微调	CSL包含 396,209 篇中文核心期刊论文元信息 （标题、摘要、关键词、学科、门类）可做预训练可构建NLP指令任务	https://github.com/ydli-ai/CSL
指令微调	alpaca code 20K代码指令数据	https://github.com/sahil280114/codealpaca#data-release
指令微调	GPT4Tools 71K GPT4指令样本	https://github.com/StevenGrove/GPT4Tools
指令微调	GPT4指令+角色扮演+代码指令	https://github.com/teknium1/GPTeacher
指令微调	Mol-Instructions 2043K 分子+蛋白质+生物分子文本指令，覆盖分子设计、蛋白质功能预测、蛋白质设计等任务	https://github.com/zjunlp/Mol-Instructions
数学	腾讯人工智能实验室发布网上爬取的数学问题APE210k	https://github.com/Chenny0808/ape210k
数学	猿辅导 AI Lab开源小学应用题Math23K	https://github.com/SCNU203/Math23k/tree/main
数学	grade school math把OpenAI的高中数学题有改造成指令样本有2-8步推理过程	https://huggingface.co/datasets/qwedsacf/grade-school-math-instructions
数学	数学问答数据集有推理过程和多项选择	https://huggingface.co/datasets/math_qa/viewer/default/test?row=2
数学	AMC竞赛数学题	https://huggingface.co/datasets/competition_math
数学	线性代数等纯数学计算题	https://huggingface.co/datasets/math_dataset
代码	APPS从不同的开放访问编码网站Codeforces、Kattis 等收集的问题	https://opendatalab.org.cn/APPS
代码	Lyra代码由带有嵌入式 SQL 的 Python 代码组成，经过仔细注释的数据库操作程序，配有中文评论和英文评论。	https://opendatalab.org.cn/Lyra
代码	Conala来自StackOverflow问题,手动注释3k，英文	https://opendatalab.org.cn/CoNaLa/download
代码	code-alpaca ChatGPT生成20K代码指令样本	https://github.com/sahil280114/codealpaca.git
代码	32K, 四种不同类型、不同难度的代码相关中文对话数据，有大模型生成，	https://github.com/zxx000728/CodeGPT
对话	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	Baize基于Chat GPT构建的self-chat数据	https://github.com/project-baize/baize-chatbot/tree/main/data
对话	FaceBook开源BlenderBot训练对话数据~6K	https://huggingface.co/datasets/blended_skill_talk
对话	AllenAI开源38.5万个对话高质量数据集SODA	https://realtoxicityprompts.apps.allenai.org/
对话	InstructDial在单一对话任务类型上进行指令微调	https://github.com/prakharguptaz/Instructdial
对话	Ultra Chat 两个独立的 ChatGPT Turbo API 进行对话，从而生成多轮对话数据	https://github.com/thunlp/UltraChat
对话	Awesome Open-domain Dialogue Models提供多个开放域对话数据	https://github.com/cingtiye/Awesome-Open-domain-Dialogue-Models#%E4%B8%AD%E6%96%87%E5%BC%80%E6%94%BE%E5%9F%9F%E5%AF%B9%E8%AF%9D%E6%95%B0%E6%8D%AE%E9%9B%86
对话	Salesforce开源超全DialogStudio	https://github.com/salesforce/DialogStudio
对话	基于事实Reference的多轮问答中文数据，已开源5万，之后会开源更多	https://github.com/sufengniu/RefGPT
RLFH	北大河狸开源RLHF数据集10K，1M需要申请	https://huggingface.co/datasets/PKU-Alignment/PKU-SafeRLHF-10K
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
RLHF	知乎问答偏好数据集	https://huggingface.co/datasets/liyucheng/zhihu_rlhf_3k
RLHF	hh-rlhf中文翻译偏好数据	https://huggingface.co/datasets/liswei/rm-static-zhTW
RLHF	面壁智能开源大规模偏好数据，基于64Kprompt使用不同模型生成4个回答使用GPT-4评估	https://github.com/OpenBMB/UltraFeedback
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
评估集	2010-2022年全国高考卷的题目	https://github.com/OpenLMLab/GAOKAO-Bench
评估集	中文通用大模型综合性评测基准SuperCLUE	https://github.com/CLUEbenchmark/SuperCLUE
英文预训练	RedPajama开源的复刻llama的预训练数据集，1.21万亿Token	https://github.com/togethercomputer/RedPajama-Data
英文预训练	Cerebras基于RedPajama进行清洗去重后得到的高质量数据集, 6270亿Token	https://huggingface.co/datasets/cerebras/SlimPajama-627B/tree/main/train
英文预训练	Pile 22个高质量数据集混合的预训练数据集800G,全量开放下载	https://pile.eleuther.ai/
通用预训练	UER整理CLUECorpusSmall+News Commentary中英	https://github.com/dbiir/UER-py/wiki/%E9%A2%84%E8%AE%AD%E7%BB%83%E6%95%B0%E6%8D%AE
中文预训练	智源人工智能开源的wudao 200G预训练数据	https://github.com/BAAI-WuDao/WuDaoMM
中文预训练	里屋社区发起开源力量收集中文互联网语料集MNBVC目标是对标ChatGPT的40T	https://github.com/esbatmop/MNBVC
中文预训练	复旦开源15万中文图书下载和抽取方案	https://github.com/FudanNLPLAB/CBook-150K
中文预训练	书生万卷数据集来自公开网页多模态数据集，包括文本，图文和视频，其中文本1T，图文150G	https://opendatalab.org.cn/OpenDataLab/WanJuan1_dot_0
中文预训练	昆仑天工开源3.2TB中英语料	https://github.com/SkyworkAI/Skywork
中文预训练	浪潮开源的用于Yuan1.0训练的预训练中文语料	https://www.airyuan.cn/home
领域预训练	度小满开源60G金融预训练语料	https://github.com/Duxiaoman-DI/XuanYuan
领域预训练	首个中文科学文献数据集CSL,也有多种NLP任务数据	https://github.com/ydli-ai/CSL
平行语料	news-commentary中英平行语料，用于中英间知识迁移	https://data.statmt.org/news-commentary/v15/training/
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
Tool-搜索增强	webCPM开源的和搜索工具进行交互问答的数据集，包括网页抽取式摘要，多事实内容回答等人工标注数据	https://github.com/thunlp/WebCPM
Tool-多工具	BmTools开源的多工具调用指令数据集	https://github.com/OpenBMB/BMTools
Tool-多工具	AgentInstruct包含6项Agent任务，包括REACT式COT标注	https://thudm.github.io/AgentTuning/
Tool-多工具	MSAgent-Bench 大模型调用数据集 598k训练数据	https://modelscope.cn/datasets/damo/MSAgent-Bench/summary
Tool-多工具	MOSS开源的知识搜索，文生图，计算器，解方程等4个插件的30万条多轮对话数据	https://github.com/OpenLMLab/MOSS#%E6%95%B0%E6%8D%AE
NL2SQL	DB-GPT-Hub梳理了多源text-to-sql数据集	https://github.com/eosphoros-ai/DB-GPT-Hub
长文本	清华开源的长文本对齐数据集LongAlign-10k	https://huggingface.co/datasets/THUDM/LongAlign-10k

AIGC

搜索

通用搜索

• 秘塔搜索: 融合了脑图，表格多模态问答的搜索应用
• You.COM : 支持多种检索增强问答模式
• Walles.AI: 融合了图像聊天，文本聊天，chatpdf，web-copilot等多种功能的智能助手
• webpilot.ai 比ChatGPT 自带的 Web Browsing更好用的浏览器检索插件，更适用于复杂搜索场景，也开通api调用了
• New Bing：需要科学上网哦
• 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

代码搜索

• devv.ai: 基于微调llama2 + RAG搭建的属于程序员的搜索引擎
• Phind: 面向开发人员的AI搜索引擎

知识管理

• glean: 企业知识搜索和项目管理类的搜索初创公司，帮助员工快速定位信息，帮助公司整合信息
• Mem: 个人知识管理，例如知识图谱，已获openai融资

ChatDoc

• Kimi-Chat: 长长长长文档理解无敌的Kimi-Chat，单文档总结多文档结构化对比，无所不能，多长都行！
• ChatDoc:ChatPDF升级版，需要科学上网，增加了表格类解析，支持选择区域的问答，在PDF识别上做的很厉害
• AskyourPdf: 同样是上传pdf进行问答和摘要的应用
• DocsGPT: 比较早出来的Chat DOC通用方案
• 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
• AlphaBox: 从个人文件夹管理出发的文档问答工具

论文研究: 日度更新，观点总结，

• SCISPACE: 论文研究的白月光，融合了全库搜索问答，以及个人上传PDF构建知识库问答。同样支持相关论文发现，和论文划词解读。并且解读内容可以保存到notebook中方便后续查找，可以说是产品和算法强强联合了。
• Consensus: AI加持的论文搜素，多论文总结，观点对比工具。产品排名巨高，但个人感觉搜索做的有提升空间
• Aminer: 论文搜索，摘要，问答，搜索关键词符号化改写；但论文知识库问答有些幻觉严重
• cool.paper: 苏神开发的基于kimi的论文阅读网站
• OpenRead: 国内产品，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
• 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
• researchgpt: 和ChatPDF类似，支持arivx论文下载，加载后对话式获取论文重点
• 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,不少功能可以借鉴
• 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

写作效率工具类

• 赛博马良:题如其名，可定制AI员工24小时全网抓取关注的创作选题，推送给小编进行二次创作
• Miracleplus: 全AI Agent负责运营的Hacker News网站
• 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
• 范文喵写作: 范文喵写作工具，选题，大纲，写作全流程
• 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
• Hix-AI: 同时提供copilot模式和综合写作模式
• AI-Write： 个人使用感较好的流程化写作工具
• 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
• Weaver AI: 波形智能开发的内容创作app，支持多场景写作
• 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
• mindShow：免费+付费的PPT制作工具，自定义PPT模板还不够好

金融垂直领域

• Alpha: ChatGPT加持的金融app，支持个股信息查询，资产分析诊断，财报汇总etc
• Composer：量化策略和AI的结合，聊天式+拖拽式投资组合构建和回测
• Finalle.ai: 实时金融数据流接入大模型
• ScopeChat:虚拟币应用，整个对话类似ChatLaw把工具组件嵌入了对话中
• AInvest：个股投资，融合BI分析，广场讨论区（有演变成雪球热度指数的赶脚)
• Reportify: 金融领域公司公告，新闻，电话会的问答和摘要总结

私人助理&聊天

• Mr.-Ranedeer-: 基于prompt和GPT-4的强大能力提供个性化学习环境，个性化出题+模型解答
• 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

Agent

• 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

视频拆条总结

• Eightify: chrome插件，节省观看长视频的时间，立即获取关键思想，分模块总结+时间戳摘要
• BibiGPT: Bilibli视频内容一键总结，多模态文档

代码copilot & BI工具

• AutoDev: AI编程辅助工具
• AlphaCodium: Flow Engineering提高代码整体通过率
• Codium: 开源的编程Copilot来啦
• Copilot: 要付费哟
• Fauxpilot: copilot本地开源替代
• Codeium: Copilot替代品，有免费版本支持各种plugin !
• 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
• Chat2DB:智能的通用数据库SQL客户端和报表工具
• ChatBI:网易数帆发布ChatBI对话数据分析平台
• Kyligence Copilot:Kyligence发布一站式指标平台的 AI 数智助理,支持对话式指标搜索，异动归因等等
• Wolverine: 代码自我debug的python脚本

多模态生成

• 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
• 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
• Magnific.ai: 两个人的团队做出的AI图片精修师
• Morph Studio: Stability AI入场视频制作
• Gamma: PPT制作神器，ProductHunt月度排名Number1

Resources

GPTs应用导航

• SimilarGPTs：全网AI产品流量大全：
• GPTSeek: 大家投票得出的最有价值的GPT应用
• ProductHunt: 技术产品网站，各类热门AI技术产品的集散地
• AI-Product-Index
• AI-Products-All-In-One
• TheRunDown: GPT应用分类
• Awesome AI Agents：Agent应用收藏
• GPT Demo
• AI-Bot各类工具导航

Prompt和其他教程类

• OpenAI Cookbook: 提供OpenAI模型使用示例 ⭐
• 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 ⭐
• 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
• Minbpe: Karpathy大佬离职openai后整了个分词器的教学代码
• LLM-VIZ: 大模型结构可视化支持GPT系列

书籍和博客类

• 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
• 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 ⭐
• LLM Powered Autonomous Agents ⭐
• All You Need to Know to Build Your First LLM App ⭐
• GPT-4 Architecture, Infrastructure, Training Dataset, Costs, Vision, MoE
• OpenAI研究员出书：为什么伟大不能被计划:
• 拾象投研机构对LLM的调研报告（文中有两次PPT的申请链接）:
• 启明创投State of Generative AI 2023
• 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? ⭐
• 符尧大佬系列新作 An Initial Exploration of Theoretical Support for Language Model Data Engineering. Part 1: Pretraining
• 奇绩创坛2023秋季路演日上创新LLM项目一览
• The Power of Prompting微软首席科学家对prompt在垂直领域使用的观点
• The Bitter Lesson 强化学习之父总结的AI研究的经验教训

会议&访谈类

• 麻省理工科技采访OpenAI工程师
• 陆奇最新演讲实录：我的大模型世界观｜第十四期
• 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
• Large Language Models (in 2023) OpenAI科学家最新大模型演讲
• OpenAI闭门会议DevDay视频 - A survey of Techniques for Maximizing LLM performance，无法翻墙可搜标题找笔记
• 月之暗面杨植麟专访,值得细读

Papers

paper List

• 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
• https://github.com/thu-coai/PaperForONLG

综述

• 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
• Large Language Models for Information Retrieval: A Survey
• AI Alignment: A Comprehensive Survey
• Trends in Integration of Knowledge and Large Language Models: A Survey and Taxonomy of Methods, Benchmarks, and Applications
• Large Models for Time Series and Spatio-Temporal Data: A Survey and Outlook
• A Survey on Language Models for Code
• Model-as-a-Service (MaaS): A Survey

大模型能力探究

• 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
  • In-Context Learning Creates Task Vectors
• 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
  • Are Emergent Abilities of Large Language Models a Mirage?
• 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?
• 领域能力
  • Capabilities of GPT-4 on Medical Challenge Problems
  • Can Generalist Foundation Models Outcompete Special-Purpose Tuning? Case Study in Medicine

Prompt Tunning范式

• 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

主流LLMS和预训练

• GLM-130B: AN OPEN BILINGUAL PRE-TRAINED MODEL
• PaLM: Scaling Language Modeling with Pathways
• PaLM 2 Technical Report
• GPT-4 Technical Report
• Backpack Language Models
• LLaMA: Open and Efficient Foundation Language Models
• Llama 2: Open Foundation and Fine-Tuned Chat Models
• Sheared LLaMA: Accelerating Language Model Pre-training via Structured Pruning
• OpenBA: An Open-sourced 15B Bilingual Asymmetric seq2seq Model Pre-trained from Scratch
• Mistral 7B
• Ziya2: Data-centric Learning is All LLMs Need
• MEGABLOCKS: EFFICIENT SPARSE TRAINING WITH MIXTURE-OF-EXPERTS
• TUTEL: ADAPTIVE MIXTURE-OF-EXPERTS AT SCALE
• Phi1- Textbooks Are All You Need ⭐
• Phi1.5- Textbooks Are All You Need II: phi-1.5 technical report
• Gemini: A Family of Highly Capable Multimodal Models
• In-Context Pretraining: Language Modeling Beyond Document Boundaries
• LLAMA PRO: Progressive LLaMA with Block Expansion
• QWEN TECHNICAL REPORT

指令微调&对齐 (instruction_tunning)

• 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
  • 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
  • Exploring the Impact of Instruction Data Scaling on Large Language Models: An Empirical Study on Real-World Use Cases
  • Scaling Relationship on Learning Mathematical Reasoning with Large Language Models
• 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
  • Improving Language Model Negotiation with Self-Play and In-Context Learning from AI Feedback
  • Human-like systematic generalization through a meta-learning neural network
  • Magicoder: Source Code Is All You Need
  • Beyond Human Data: Scaling Self-Training for Problem-Solving with Language Models
  • Generative Representational Instruction Tuning
• 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 .
  • 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
  • Improving Text Embeddings with Large Language Models
• 如何降低通用能力损失
  • How Abilities in Large Language Models are Affected by Supervised Fine-tuning Data Composition
  • TWO-STAGE LLM FINE-TUNING WITH LESS SPECIALIZATION AND MORE GENERALIZATION
• 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
• Others
  • Crosslingual Generalization through Multitask Finetuning
  • Cross-Task Generalization via Natural Language Crowdsourcing Instructions
  • UNIFIEDSKG: Unifying and Multi-Tasking Structured Knowledge Grounding with Text-to-Text Language Models
  • PromptSource: An Integrated Development Environment and Repository for Natural Language Prompts
  • ROLELLM: BENCHMARKING, ELICITING, AND ENHANCING ROLE-PLAYING ABILITIES OF LARGE LANGUAGE MODELS

对话模型

• 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

思维链 (prompt_chain_of_thought)

• 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
  • Progressive-Hint Prompting Improves Reasoning in Large Language Models
  • LARGE LANGUAGE MODELS CAN LEARN RULES
  • DIVERSITY OF THOUGHT IMPROVES REASONING ABILITIES OF LARGE LANGUAGE MODELS
  • From Complex to Simple: Unraveling the Cognitive Tree for Reasoning with Small Language Models
  • Take a Step Back: Evoking Reasoning via Abstraction in Large Language Models
  • LARGE LANGUAGE MODELS AS OPTIMIZERS
• 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
  • Chain of Code: Reasoning with a Language Model-Augmented Code Emulator
• 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
  • Chain-of-Thought Reasoning Without Prompting
• 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

RLHF

• Deepmind
  • Teaching language models to support answers with verified quotes
  • sparrow, Improving alignment of dialogue agents via targetd human judgements ⭐
  • STATISTICAL REJECTION SAMPLING IMPROVES PREFERENCE OPTIMIZATION
  • Reinforced Self-Training (ReST) for Language Modeling
  • SLiC-HF: Sequence Likelihood Calibration with Human Feedback
  • CALIBRATING SEQUENCE LIKELIHOOD IMPROVES CONDITIONAL LANGUAGE GENERATION
  • REWARD DESIGN WITH LANGUAGE MODELS
  • Final-Answer RL Solving math word problems with processand outcome-based feedback
• 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 ⭐
  • WEAK-TO-STRONG GENERALIZATION: ELICITING STRONG CAPABILITIES WITH WEAK SUPERVISION ⭐
  • PRM：Let's verify step by step
  • • OpenAI Super Alignment Blog
• 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
  • Sleeper Agents: Training Deceptive LLMs that Persist Through Safety Trainin
• 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
  • Chain of Hindsight Aligns Language Models with Feedback
  • AlpacaFarm: A Simulation Framework for Methods that Learn from Human Feedback
  • RAFT: Reward rAnked FineTuning for Generative Foundation Model Alignment
  • RLAIF: Scaling Reinforcement Learning from Human Feedback with AI Feedback
  • Training Socially Aligned Language Models in Simulated Human Society
  • RAIN: Your Language Models Can Align Themselves without Finetuning
  • Generative Judge for Evaluating Alignment
  • PEERING THROUGH PREFERENCES: UNRAVELING FEEDBACK ACQUISITION FOR ALIGNING LARGE LANGUAGE MODELS
  • SALMON: SELF-ALIGNMENT WITH PRINCIPLE-FOLLOWING REWARD MODELS
  • Large Language Model Unlearning ⭐
  • ADVERSARIAL PREFERENCE OPTIMIZATION ⭐
  • Preference Ranking Optimization for Human Alignment
  • A Long Way to Go: Investigating Length Correlations in RLHF
  • ENABLE LANGUAGE MODELS TO IMPLICITLY LEARN SELF-IMPROVEMENT FROM DATA
  • REWARD MODEL ENSEMBLES HELP MITIGATE OVEROPTIMIZATION
  • LEARNING OPTIMAL ADVANTAGE FROM PREFERENCES AND MISTAKING IT FOR REWARD
  • ULTRAFEEDBACK: BOOSTING LANGUAGE MODELS WITH HIGH-QUALITY FEEDBACK
  • MOTIF: INTRINSIC MOTIVATION FROM ARTIFICIAL INTELLIGENCE FEEDBACK
  • STABILIZING RLHF THROUGH ADVANTAGE MODEL AND SELECTIVE REHEARSAL
  • Shepherd: A Critic for Language Model Generation
  • LEARNING TO GENERATE BETTER THAN YOUR LLM
  • Fine-Grained Human Feedback Gives Better Rewards for Language Model Training
  • Principle-Driven Self-Alignment of Language Models from Scratch with Minimal Human Supervision
  • Direct Preference Optimization: Your Language Model is Secretly a Reward Model
  • HIR The Wisdom of Hindsight Makes Language Models Better Instruction Followers
• RL探究
  • UNDERSTANDING THE EFFECTS OF RLHF ON LLM GENERALISATION AND DIVERSITY
  • A LONG WAY TO GO: INVESTIGATING LENGTH CORRELATIONS IN RLHF
  • THE TRICKLE-DOWN IMPACT OF REWARD (IN-)CONSISTENCY ON RLHF
  • Open Problems and Fundamental Limitations of Reinforcement Learning from Human Feedback
  • HUMAN FEEDBACK IS NOT GOLD STANDARD
  • CONTRASTIVE POST-TRAINING LARGE LANGUAGE MODELS ON DATA CURRICULUM

LLM Agent 让模型使用工具 (llm_agent)

• A Survey on Large Language Model based Autonomous Agents
• PERSONAL LLM AGENTS: INSIGHTS AND SURVEY ABOUT THE CAPABILITY, EFFICIENCY AND SECURITY
• 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 ⭐
  • 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
  • InstructTODS: Large Language Models for End-to-End Task-Oriented Dialogue Systems
  • TPTU: Task Planning and Tool Usage of Large Language Model-based AI Agents
  • ControlLLM: Augment Language Models with Tools by Searching on Graphs
  • Reflexion: an autonomous agent with dynamic memory and self-reflection
  • AutoAgents: A Framework for Automatic Agent Generation
  • GitAgent: Facilitating Autonomous Agent with GitHub by Tool Extension
• 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
  • AgentTuning: Enabling Generalized Agent Abilities for LLMs
  • SWIFTSAGE: A Generative Agent with Fast and Slow Thinking for Complex Interactive Tasks
  • FireAct: Toward Language Agent Fine-tuning
  • Pangu-Agent: A Fine-Tunable Generalist Agent with Structured Reasoning
  • REST MEETS REACT: SELF-IMPROVEMENT FOR MULTI-STEP REASONING LLM AGENT
  • Efficient Tool Use with Chain-of-Abstraction Reasoning
• 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
  • Demonstration of InsightPilot: An LLM-Empowered Automated Data Exploration System
  • GeneGPT: Augmenting Large Language Models with Domain Tools for Improved Access to Biomedical Information
  • PointLLM: Empowering Large Language Models to Understand Point Clouds
  • Interpretable Long-Form Legal Question Answering with Retrieval-Augmented Large Language Models
  • Generating Explanations in Medical Question-Answering by Expectation Maximization Inference over Evidence
  • CarExpert: Leveraging Large Language Models for In-Car Conversational Question Answering
  • A Multimodal Foundation Agent for Financial Trading: Tool-Augmented, Diversified, and Generalist
• 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
  • Automatic Evaluation of Attribution by Large Language Models
  • Benchmarking Large Language Models in Retrieval-Augmented Generation
  • ARES: An Automated Evaluation Framework for Retrieval-Augmented Generation Systems
• 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
  • Communicative Agents for Software Development ⭐
  • METAAGENTS: SIMULATING INTERACTIONS OF HUMAN BEHAVIORS FOR LLM-BASED TASK-ORIENTED COORDINATION VIA COLLABORATIVE GENERATIVE AGENTS
  • LET MODELS SPEAK CIPHERS: MULTIAGENT DEBATE THROUGH EMBEDDINGS
  • MedAgents: Large Language Models as Collaborators for Zero-shot Medical Reasoning
  • War and Peace (WarAgent): Large Language Model-based Multi-Agent Simulation of World Wars
  • More Agents Is All You Need
• 自主学习和探索
  • AppAgent: Multimodal Agents as Smartphone Users
  • Investigate-Consolidate-Exploit: A General Strategy for Inter-Task Agent Self-Evolution
  • https://blog.x-agent.net/blog/xagent/
• 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
  • LLaMA Rider: Spurring Large Language Models to Explore the Open World

RAG

• 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
• SELF-RAG: LEARNING TO RETRIEVE, GENERATE, AND CRITIQUE THROUGH SELF-REFLECTION ⭐
• Walking Down the Memory Maze: Beyond Context Limit through Interactive Reading ⭐
• Demonstrate-Search-Predict: Composing retrieval and language models for knowledge-intensive NLP
• Search-in-the-Chain: Towards Accurate, Credible and Traceable Large Language Models for Knowledge-intensive Tasks
• Active Retrieval Augmented Generation
• kNN-LM Does Not Improve Open-ended Text Generation
• Can Retriever-Augmented Language Models Reason? The Blame Game Between the Retriever and the Language Model
• Query2doc: Query Expansion with Large Language Models ⭐
• RLCF：Aligning the Capabilities of Large Language Models with the Context of Information Retrieval via Contrastive Feedback
• Augmented Embeddings for Custom Retrievals
• DORIS-MAE: Scientific Document Retrieval using Multi-level Aspect-based Queries
• Learning to Filter Context for Retrieval-Augmented Generation
• THINK-ON-GRAPH: DEEP AND RESPONSIBLE REASON- ING OF LARGE LANGUAGE MODEL ON KNOWLEDGE GRAPH
• RA-DIT: RETRIEVAL-AUGMENTED DUAL INSTRUCTION TUNING
• Query Expansion by Prompting Large Language Models ⭐
• CHAIN-OF-NOTE: ENHANCING ROBUSTNESS IN RETRIEVAL-AUGMENTED LANGUAGE MODELS
• IAG: Induction-Augmented Generation Framework for Answering Reasoning Questions
• T2Ranking: A large-scale Chinese Benchmark for Passage Ranking
• Factuality Enhanced Language Models for Open-Ended Text Generation
• FRESHLLMS: REFRESHING LARGE LANGUAGE MODELS WITH SEARCH ENGINE AUGMENTATION
• KwaiAgents: Generalized Information-seeking Agent System with Large Language Models
• Rich Knowledge Sources Bring Complex Knowledge Conflicts: Recalibrating Models to Reflect Conflicting Evidence
• Complex Claim Verification with Evidence Retrieved in the Wild
• Retrieval-Augmented Generation for Large Language Models: A Survey
• Enhancing Retrieval-Augmented Large Language Models with Iterative Retrieval-Generation Synergy
• ChatQA: Building GPT-4 Level Conversational QA Models
• RAG vs Fine-tuning: Pipelines, Tradeoffs, and a Case Study on Agriculture
• Benchmarking Large Language Models in Retrieval-Augmented Generation
• HyDE：Precise Zero-Shot Dense Retrieval without Relevance Labels
• PROMPTAGATOR : FEW-SHOT DENSE RETRIEVAL FROM 8 EXAMPLES
• SYNERGISTIC INTERPLAY BETWEEN SEARCH AND LARGE LANGUAGE MODELS FOR INFORMATION RETRIEVAL
• T-RAG: Lessons from the LLM Trenches
• ASK THE RIGHT QUESTIONS:ACTIVE QUESTION REFORMULATION WITH REINFORCEMENT LEARNING [传统方案参考]
• Query Expansion Techniques for Information Retrieval a Survey [传统方案参考]
• Learning to Rewrite Queries [传统方案参考]
• Managing Diversity in Airbnb Search[传统方案参考]
• 新向量模型用于Recall和Ranking
  • BGE M3-Embedding: Multi-Lingual, Multi-Functionality, Multi-Granularity Text Embeddings Through Self-Knowledge Distillation
  • 网易为RAG设计的BCE Embedding技术报告

LLM+KG

• 综述类
  • Unifying Large Language Models and Knowledge Graphs: A Roadmap
  • Large Language Models and Knowledge Graphs: Opportunities and Challenges
  • 知识图谱与大模型融合实践研究报告2023
• KG用于大模型推理
  • Using Large Language Models for Zero-Shot Natural Language Generation from Knowledge Graphs
  • MindMap: Knowledge Graph Prompting Sparks Graph of Thoughts in Large Language Models
  • Knowledge-Augmented Language Model Prompting for Zero-Shot Knowledge Graph Question Answering
  • Domain Specific Question Answering Over Knowledge Graphs Using Logical Programming and Large Language Models
  • BRING YOUR OWN KG: Self-Supervised Program Synthesis for Zero-Shot KGQA
  • StructGPT: A General Framework for Large Language Model to Reason over Structured Data
• 大模型用于KG构建
  • Enhancing Knowledge Graph Construction Using Large Language Models
  • LLM-assisted Knowledge Graph Engineering: Experiments with ChatGPT
  • ITERATIVE ZERO-SHOT LLM PROMPTING FOR KNOWLEDGE GRAPH CONSTRUCTION
  • Exploring Large Language Models for Knowledge Graph Completion

Humanoid Agents

• HABITAT 3.0: A CO-HABITAT FOR HUMANS, AVATARS AND ROBOTS
• Humanoid Agents: Platform for Simulating Human-like Generative Agents
• Voyager: An Open-Ended Embodied Agent with Large Language Models
• Shaping the future of advanced robotics
• AUTORT: EMBODIED FOUNDATION MODELS FOR LARGE SCALE ORCHESTRATION OF ROBOTIC AGENTS
• ROBOTIC TASK GENERALIZATION VIA HINDSIGHT TRAJECTORY SKETCHES

预训练数据(pretrain_data)

• 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
• In-Context Pretraining: Language Modeling Beyond Document Boundaries

领域模型 (domain_llms)

• 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
• LLEMMA: AN OPEN LANGUAGE MODEL FOR MATHEMATICS
• CFBenchmark: Chinese Financial Assistant Benchmark for Large Language Model
• InvestLM: A Large Language Model for Investment using Financial Domain Instruction Tuning
• WeaverBird: Empowering Financial Decision-Making with Large Language Model, Knowledge Base, and Search Engine
• FinMem: A Performance-Enhanced LLM Trading Agent with Layered Memory and Character Design
• MEDITAB: SCALING MEDICAL TABULAR DATA PREDICTORS VIA DATA CONSOLIDATION, ENRICHMENT, AND REFINEMENT
• PLLaMa: An Open-source Large Language Model for Plant Science

LLM超长文本处理 (long_input)

• 位置编码、注意力机制优化
  • Unlimiformer: Long-Range Transformers with Unlimited Length Input
  • Parallel Context Windows for Large Language Models
  • 苏剑林, NBCE：使用朴素贝叶斯扩展LLM的Context处理长度 ⭐
  • Structured Prompting: Scaling In-Context Learning to 1,000 Examples
  • Vcc: Scaling Transformers to 128K Tokens or More by Prioritizing Important Tokens
  • Scaling Transformer to 1M tokens and beyond with RMT
  • TRAIN SHORT, TEST LONG: ATTENTION WITH LINEAR BIASES ENABLES INPUT LENGTH EXTRAPOLATION ⭐
  • 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？
  • 苏剑林,Transformer升级之路：15、Key归一化助力长度外推
  • EFFICIENT STREAMING LANGUAGE MODELS WITH ATTENTION SINKS
  • Ring Attention with Blockwise Transformers for Near-Infinite Context
  • YaRN: Efficient Context Window Extension of Large Language Models
  • LM-INFINITE: SIMPLE ON-THE-FLY LENGTH GENERALIZATION FOR LARGE LANGUAGE MODELS
  • EFFICIENT STREAMING LANGUAGE MODELS WITH ATTENTION SINKS
• 上文压缩排序方案
  • Lost in the Middle: How Language Models Use Long Contexts ⭐
  • LLMLingua: Compressing Prompts for Accelerated Inference of Large Language Models
  • LongLLMLingua: Accelerating and Enhancing LLMs in Long Context Scenarios via Prompt Compression ⭐
  • Learning to Compress Prompts with Gist Tokens
  • Unlocking Context Constraints of LLMs: Enhancing Context Efficiency of LLMs with Self-Information-Based Content Filtering
  • LongAgent: Scaling Language Models to 128k Context through Multi-Agent Collaboration
• 训练和模型架构方案
  • Never Train from Scratch: FAIR COMPARISON OF LONGSEQUENCE MODELS REQUIRES DATA-DRIVEN PRIORS
  • Soaring from 4K to 400K: Extending LLM's Context with Activation Beacon
  • Never Lost in the Middle: Improving Large Language Models via Attention Strengthening Question Answering
  • Focused Transformer: Contrastive Training for Context Scaling
  • Effective Long-Context Scaling of Foundation Models
  • ON THE LONG RANGE ABILITIES OF TRANSFORMERS
  • Efficient Long-Range Transformers: You Need to Attend More, but Not Necessarily at Every Layer
  • POSE: EFFICIENT CONTEXT WINDOW EXTENSION OF LLMS VIA POSITIONAL SKIP-WISE TRAINING
  • LONGLORA: EFFICIENT FINE-TUNING OF LONGCONTEXT LARGE LANGUAGE MODELS
  • LongAlign: A Recipe for Long Context Alignment of Large Language Models
  • Data Engineering for Scaling Language Models to 128K Context
• 效率优化
  • Efficient Attention: Attention with Linear Complexities
  • Transformers are RNNs: Fast Autoregressive Transformers with Linear Attention
  • HyperAttention: Long-context Attention in Near-Linear Time
  • FlashAttention: Fast and Memory-Efficient Exact Attention with IO-Awareness
  • With Greater Text Comes Greater Necessity: Inference-Time Training Helps Long Text Generation

LLM长文本生成（long_output）

• Re3 : Generating Longer Stories With Recursive Reprompting and Revision
• RECURRENTGPT: Interactive Generation of (Arbitrarily) Long Text
• DOC: Improving Long Story Coherence With Detailed Outline Control
• Weaver: Foundation Models for Creative Writing
• Assisting in Writing Wikipedia-like Articles From Scratch with Large Language Models

NL2SQL

• 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

Code Generation

• Code Generation with AlphaCodium: From Prompt Engineering to Flow Engineering
• Codeforces as an Educational Platform for Learning Programming in Digitalization
• Competition-Level Code Generation with AlphaCode
• CODECHAIN: TOWARDS MODULAR CODE GENERATION THROUGH CHAIN OF SELF-REVISIONS WITH REPRESENTATIVE SUB-MODULES

降低模型幻觉 (reliability)

• Survey
  • Large language models and the perils of their hallucinations
  • Survey of Hallucination in Natural Language Generation
  • Siren's Song in the AI Ocean: A Survey on Hallucination in Large Language Models
  • A Survey of Hallucination in Large Foundation Models
  • A Survey on Hallucination in Large Language Models: Principles, Taxonomy, Challenges, and Open Questions
  • Calibrated Language Models Must Hallucinate
  • Why Does ChatGPT Fall Short in Providing Truthful Answers?
• Prompt or Tunning
  • R-Tuning: Teaching Large Language Models to Refuse Unknown Questions
  • PROMPTING GPT-3 TO BE RELIABLE
  • ASK ME ANYTHING: A SIMPLE STRATEGY FOR PROMPTING LANGUAGE MODELS ⭐
  • On the Advance of Making Language Models Better Reasoners
  • RefGPT: Reference → Truthful & Customized Dialogues Generation by GPTs and for GPTs
  • Rethinking with Retrieval: Faithful Large Language Model Inference
  • GENERATE RATHER THAN RETRIEVE: LARGE LANGUAGE MODELS ARE STRONG CONTEXT GENERATORS
  • Large Language Models Struggle to Learn Long-Tail Knowledge
• Decoding Strategy
  • Trusting Your Evidence: Hallucinate Less with Context-aware Decoding ⭐
  • SELF-REFINE:ITERATIVE REFINEMENT WITH SELF-FEEDBACK ⭐
  • Enhancing Self-Consistency and Performance of Pre-Trained Language Models through Natural Language Inference
  • Inference-Time Intervention: Eliciting Truthful Answers from a Language Model
  • Enabling Large Language Models to Generate Text with Citations
  • Factuality Enhanced Language Models for Open-Ended Text Generation
  • KL-Divergence Guided Temperature Sampling
  • KCTS: Knowledge-Constrained Tree Search Decoding with Token-Level Hallucination Detection
  • CONTRASTIVE DECODING IMPROVES REASONING IN LARGE LANGUAGE MODEL
  • Contrastive Decoding: Open-ended Text Generation as Optimization
• Probing and Detection
  • Automatic Evaluation of Attribution by Large Language Models
  • QAFactEval: Improved QA-Based Factual Consistency Evaluation for Summarization
  • Zero-Resource Hallucination Prevention for Large Language Models
  • LLM Lies: Hallucinations are not Bugs, but Features as Adversarial Examples
  • Language Models (Mostly) Know What They Know ⭐
  • LM vs LM: Detecting Factual Errors via Cross Examination
  • Do Language Models Know When They’re Hallucinating References?
  • SELFCHECKGPT: Zero-Resource Black-Box Hallucination Detection for Generative Large Language Models
  • SELF-CONTRADICTORY HALLUCINATIONS OF LLMS: EVALUATION, DETECTION AND MITIGATION
  • Self-consistency for open-ended generations
  • Improving Factuality and Reasoning in Language Models through Multiagent Debate
  • Selective-LAMA: Selective Prediction for Confidence-Aware Evaluation of Language Models
  • Can LLMs Express Their Uncertainty? An Empirical Evaluation of Confidence Elicitation in LLMs
• Reviewing and Calibration
  • Truth-o-meter: Collaborating with llm in fighting its hallucinations
  • RARR: Researching and Revising What Language Models Say, Using Language Models
  • CRITIC: LARGE LANGUAGE MODELS CAN SELFCORRECT WITH TOOL-INTERACTIVE CRITIQUING
  • VALIDATING LARGE LANGUAGE MODELS WITH RELM
  • PURR: Efficiently Editing Language Model Hallucinations by Denoising Language Model Corruptions
  • Check Your Facts and Try Again: Improving Large Language Models with External Knowledge and Automated Feedback
  • Adaptive Chameleon or Stubborn Sloth: Unraveling the Behavior of Large Language Models in Knowledge Clashes
  • Woodpecker: Hallucination Correction for Multimodal Large Language Models
  • Zero-shot Faithful Factual Error Correction

大模型评估（evaluation）

• 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
  • FACTSCORE: Fine-grained Atomic Evaluation of Factual Precision in Long Form Text Generation
  • KoLA: Carefully Benchmarking World Knowledge of Large Language Models
  • When Not to Trust Language Models: Investigating Effectiveness of Parametric and Non-Parametric Memories
  • FACTOOL: Factuality Detection in Generative AI A Tool Augmented Framework for Multi-Task and Multi-Domain Scenarios
• 检测任务
  • Detecting Pretraining Data from Large Language Models
  • Scalable Extraction of Training Data from (Production) Language Models
  • Rethinking Benchmark and Contamination for Language Models with Rephrased Samples

推理优化(inference)

• 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

模型知识编辑黑科技(model_edit)

• 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
• Language Models are Super Mario: Absorbing Abilities from Homologous Models as a Free Lunch

模型合并黑科技(model_merge)

• Blending Is All You Need: Cheaper, Better Alternative to Trillion-Parameters LLM
• DARE Language Models are Super Mario: Absorbing Abilities from Homologous Models as a Free Lunch
• EDITING MODELS WITH TASK ARITHMETIC
• TIES-Merging: Resolving Interference When Merging Models
• LM-Cocktail: Resilient Tuning of Language Models via Model Merging

Other Prompt Engineer(prompt_engineer)

• 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

Multimodal

• 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
• Mirasol3B: A Multimodal Autoregressive model for time-aligned and contextual modalities
• Vary: Scaling up the Vision Vocabulary for Large Vision-Language Models
• AnyGPT: Unified Multimodal LLM with Discrete Sequence Modeling
• Sora tech report

Timeseries LLM

• TimeGPT-1
• Large Models for Time Series and Spatio-Temporal Data: A Survey and Outlook
• TIME-LLM: TIME SERIES FORECASTING BY REPROGRAMMING LARGE LANGUAGE MODELS
• Large Language Models Are Zero-Shot Time Series Forecasters
• TEMPO: PROMPT-BASED GENERATIVE PRE-TRAINED TRANSFORMER FOR TIME SERIES FORECASTING
• Generative Pre-Training of Time-Series Data for Unsupervised Fault Detection in Semiconductor Manufacturing
• Lag-Llama: Towards Foundation Models for Time Series Forecasting
• PromptCast: A New Prompt-based Learning Paradigm for Time Series Forecasting

Quanization

• AWQ: Activation-aware Weight Quantization for LLM Compression and Acceleration
• LLM-QAT: Data-Free Quantization Aware Training for Large Language Models
• LLM.int8() 8-bit Matrix Multiplication for Transformers at Scale
• SmoothQuant Accurate and Efficient Post-Training Quantization for Large Language Models

Adversarial Attacking

• Curiosity-driven Red-teaming for Large Language Models
• Red Teaming Language Models with Language Models
• EXPLORE, ESTABLISH, EXPLOIT: RED-TEAMING LANGUAGE MODELS FROM SCRATCH

Others

• 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