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

• 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
• 解密prompt系列25. RLHF改良方案之样本标注：RLAIF & SALMON
• 解密prompt系列26. 人类思考vs模型思考：抽象和发散思维

• 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
• sider.ai: 支持多模型浏览器插件对话和多模态交互操作

• 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

• 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
• GPT-Crawler: 通过简单配置，即可自行提取网页的文本信息构建知识库，并进一步自定义GPTs
• ChatInsight: 企业级文档管理，和基于文档的对话

• 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

• 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

• 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
• 研墨AI: 面向咨询领域的内容创作应用
• 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

• 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

• 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

• 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

• 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

• 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
• DataHerald: Text2SQL

• 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

• 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

• 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

• 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

• 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

• 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
• https://github.com/khuangaf/Awesome-Chart-Understanding

• 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

• 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

• 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
• 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

• 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
• SFT数据Scaling Law
  • 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
  • WHEN SCALING MEETS LLM FINETUNING: THE EFFECT OF DATA, MODEL AND FINETUNING METHOD
• 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
  • InsCL: A Data-efficient Continual Learning Paradigm for Fine-tuning Large Language Models with Instructions
• 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

• 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
  • 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

• 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
  • Solving math word problems with process- and outcome-based feedback
  • Beyond Human Data: Scaling Self-Training for Problem-Solving with Language Models
• 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
  • Training Verifiers to Solve Math Word Problems [PRM的前置依赖]
  • 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
  • Aligner: Achieving Efficient Alignment through Weak-to-Strong Correction
• 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

• 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
  • PreAct: Predicting Future in ReAct Enhances Agent's Planning Ability
  • TOOLLLM: FACILITATING LARGE LANGUAGE MODELS TO MASTER 16000+ REAL-WORLD APIS -AnyTool: Self-Reflective, Hierarchical Agents for Large-Scale API Calls
  • AIOS: LLM Agent Operating System
• 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
  • Agent-FLAN: Designing Data and Methods of Effective Agent Tuning for Large Language Models
  • AgentOhana: Design Unified Data and Training Pipeline for Effective Agent Learning
  • Agent Lumos: Unified and Modular Training for Open-Source Language Agents
• 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
  • 数据分析
    • DS-Agent: Automated Data Science by Empowering Large Language Models with Case-Based Reasoning
    • InsightLens: Discovering and Exploring Insights from Conversational Contexts in Large-Language-Model-Powered Data Analysis
    • Data-Copilot: Bridging Billions of Data and Humans with Autonomous Workflow
    • Demonstration of InsightPilot: An LLM-Empowered Automated Data Exploration System
  • 金融
    • 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
    • AlphaFin：使用检索增强股票链框架对财务分析进行基准测试
    • A Multimodal Foundation Agent for Financial Trading: Tool-Augmented, Diversified, and Generalist
    • Can Large Language Models Beat Wall Street? Unveiling the Potential of AI in stock Selection
  • 生物医疗
    • GeneGPT: Augmenting Large Language Models with Domain Tools for Improved Access to Biomedical Information
    • ChemCrow Augmenting large language models with chemistry tools
    • Generating Explanations in Medical Question-Answering by Expectation Maximization Inference over Evidence
  • 其他
    • WebShop: Towards Scalable Real-World Web Interaction with Grounded Language Agents
    • ToolkenGPT: Augmenting Frozen Language Models with Massive Tools via Tool Embeddings
    • PointLLM: Empowering Large Language Models to Understand Point Clouds
    • Interpretable Long-Form Legal Question Answering with Retrieval-Augmented Large Language Models
    • CarExpert: Leveraging Large Language Models for In-Car Conversational Question Answering
• 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
  • LLMs in the Imaginarium: Tool Learning through Simulated Trial and Error
  • Empowering Large Language Model Agents through Action Learning
  • Trial and Error: Exploration-Based Trajectory Optimization for LLM Agents
• 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
  • LLMs Can’t Plan, But Can Help Planning in LLM-Modulo Frameworks

• 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
• RAT: Retrieval Augmented Thoughts Elicit Context-Aware Reasoning in Long-Horizon Generation
• ARAGOG: Advanced RAG Output Grading
• 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
  • BGE Landmark Embedding: A Chunking-Free Embedding Method For Retrieval Augmented Long-Context Large Language Models
• Contextual.ai-RAG2.0

• 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

• 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

• 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
• Data Mixing Laws: Optimizing Data Mixtures by Predicting Language Modeling Performance

• 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
• MEDITAB: SCALING MEDICAL TABULAR DATA PREDICTORS VIA DATA CONSOLIDATION, ENRICHMENT, AND REFINEMENT
• PLLaMa: An Open-source Large Language Model for Plant Science

• 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

• 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

• 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 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

• 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

• 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
  • LONG-FORM FACTUALITY IN LARGE LANGUAGE MODELS
• 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

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

• 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
• SLICEGPT: COMPRESS LARGE LANGUAGE MODELS BY DELETING ROWS AND COLUMNS
• Checkpoint Merging via Bayesian Optimization in LLM Pretrainin

• 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
• Prompts As Programs: A Structure-Aware Approach to Efficient Compile-Time Prompt Optimization

• InstructBLIP: Towards General-purpose Vision-Language Models with Instruction Tuning
• Visual ChatGPT: Talking, Drawing and Editing with Visual Foundation Models
• LLava Visual Instruction Tuning
• MiniGPT-4: Enhancing Vision-Language Understanding with Advanced 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
• PaLM-E: An Embodied Multimodal Language Model
• TabLLM: Few-shot Classification of Tabular Data with Large Language Models
• AnyGPT: Unified Multimodal LLM with Discrete Sequence Modeling
• Sora tech report
• Towards General Computer Control: A Multimodal Agent for Red Dead Redemption II as a Case Study
• OCR
  • Vary: Scaling up the Vision Vocabulary for Large Vision-Language Models
  • Large OCR Model:An Empirical Study of Scaling Law for OCR
  • ON THE HIDDEN MYSTERY OF OCR IN LARGE MULTIMODAL MODELS

• 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

• 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

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

• 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