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~

• 开源模型和评测榜单
• 开源推理，微调，Agent，RAG，propmt 框架
• 开源SFT，RLHF，Pretrain 数据集
• AIGC各领域应用汇总
• Prompt教程，经典博客和AI会议访谈

• 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. COT：模型复杂推理-思维链基础和进阶玩法
• 解密Prompt系列10. COT：思维链COT原理探究
• 解密Prompt系列11. COT：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. RAG之再谈召回多样性优化
• 解密Prompt系列21. RAG之再谈召回信息密度和质量
• ​解密Prompt系列22. 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
• 解密prompt系列27. LLM对齐经验之如何降低通用能力损失
• 解密Prompt系列28. LLM Agent之金融领域智能体：FinMem & FinAgent
• 解密Prompt系列29. LLM Agent之真实世界海量API解决方案：ToolLLM & AnyTool
• 解密Prompt系列30. LLM Agent之互联网冲浪智能体们
• ​解密Prompt系列31. LLM Agent之从经验中不断学习的智能体
• 解密Prompt系列32. LLM之表格理解任务-文本模态
• 解密Prompt系列33. LLM之图表理解任务-多模态篇
• ​解密prompt系列34. RLHF之训练另辟蹊径：循序渐进 & 青出于蓝
• 解密prompt系列35. Prompt标准化进行时！ DSPy论文串烧和代码示例
• 解密Prompt系列36. Prompt结构化编写和最优化算法UNIPROMPT
• 解密Prompt系列37. RAG之前置决策何时联网的多种策略
• 解密Prompt系列38. 多Agent路由策略
• 解密prompt系列39. RAG之借助LLM优化精排环节
• 解密prompt系列40. LLM推理scaling Law
• 解密prompt系列41. GraphRAG真的是Silver Bullet？
• 解密prompt系列42. LLM通往动态复杂思维链之路
• 解密prompt系列43. LLM Self Critics
• 解密prompt系列44. RAG探索模式？深度思考模式？
• 解密Prompt系列45. 再探LLM Scalable Oversight -辩论、博弈哪家强
• 解密prompt系列46. LLM结构化输出代码示例和原理分析
• 解密prompt系列47. O1 Long Thought的一些特征分析
• ​解密prompt系列48. DeepSeek R1 & Kimi 1.5长思维链 - RL Scaling
• ​解密prompt系列49. 回顾R1之前的思维链发展
• 解密prompt系列50. RL用于优化Agent行为路径的一些思路
• 解密prompt系列51. R1实验的一些细节讨论
• 解密prompt系列52. 闲聊大模型还有什么值得探索的领域
• 解密prompt系列53. 再谈大模型Memory
• 解密prompt系列54.Context Cache代码示例和原理分析
• 解密prompt系列55.Agent Memory的工程实现 - Mem0 & LlamaIndex

• 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
• https://github.com/srush/awesome-o1/?tab=readme-ov-file

• 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
• Phi-3 Technical Report: A Highly Capable Language Model Locally on Your Phone
• 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
• Fewer Truncations Improve Language Modeling
• ChatGLM: A Family of Large Language Models from GLM-130B to GLM-4 All Tools
• Phi-4 Technical Report
• Byte Latent Transformer: Patches Scale Better Than Tokens
• Qwen2.5 Technical Report
• DeepSeek-V3 Technical Report
• Mixtral of Experts
• DeepSeek_R1 ⭐
• KIMI K1.5: SCALING REINFORCEMENT LEARNING WITH LLMS ⭐

• 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 ⭐
  • 【TOT】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
  • 【Verify-and-Edit】: A Knowledge-Enhanced Chain-of-Thought Framework
  • 【GOT】Beyond Chain-of-Thought, Effective Graph-of-Thought Reasoning in Large Language Models
  • 【TOMT】Tree-of-Mixed-Thought: Combining Fast and Slow Thinking for Multi-hop Visual Reasoning
  • 【LAMBADA】: Backward Chaining for Automated Reasoning in Natural Language
  • 【AOT】Algorithm of Thoughts: Enhancing Exploration of Ideas in Large Language Models ⭐
  • 【GOT】Graph of Thoughts: Solving Elaborate Problems with Large Language Models ⭐
  • 【PHP】Progressive-Hint Prompting Improves Reasoning in Large Language Models
  • 【HtT】LARGE LANGUAGE MODELS CAN LEARN RULES ⭐
  • 【DIVSE】DIVERSITY OF THOUGHT IMPROVES REASONING ABILITIES OF LARGE LANGUAGE MODELS
  • 【CogTree】From Complex to Simple: Unraveling the Cognitive Tree for Reasoning with Small Language Models
  • 【Step-Back】Take a Step Back: Evoking Reasoning via Abstraction in Large Language Models ⭐
  • 【OPRO】LARGE LANGUAGE MODELS AS OPTIMIZERS ⭐
  • 【BOT】Buffer of Thoughts: Thought-Augmented Reasoning with Large Language Models
  • Abstraction-of-Thought Makes Language Models Better Reasoners
  • 【SymbCoT】Faithful Logical Reasoning via Symbolic Chain-of-Thought
  • 【XOT】EVERYTHING OF THOUGHTS : DEFYING THE LAW OF PENROSE TRIANGLE FOR THOUGHT GENERATION
  • 【IoT】Iteration of Thought: Leveraging Inner Dialogue for Autonomous Large Language Model Reasoning
  • 【DOT】On the Diagram of Thought
  • 【ROT】Reversal of Thought: Enhancing Large Language Models with Preference-Guided Reverse Reasoning Warm-up.
  • Thinking Forward and Backward: Effective Backward Planning with Large Language Models
  • 【KR】K-Level Reasoning: Establishing Higher Order Beliefs in Large Language Models for Strategic Reasoning
  • 【Self-Discover】SELF-DISCOVER: Large Language Models Self-Compose Reasoning Structures
  • 【Theory-of-Mind】HOW FAR ARE LARGE LANGUAGE MODELS FROMAGENTS WITH THEORY-OF-MIND?
  • 【PC-SUBQ】Prompting Strategies for Enabling Large Language Models to Infer Causation from Correlation
  • Reverse Thinking Makes LLMs Stronger Reasoners
  • Chain of Draft: Thinking Faster by Writing Less
  • Atom of Thoughts for Markov LLM Test-Time Scaling
• 非传统COT问题分解方向
  • Decomposed Prompting A MODULAR APPROACH FOR Solving Complex Tasks
  • Successive Prompting for Decomposing Complex Questions
• 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
  • Chain of Code: Reasoning with a Language Model-Augmented Code Emulator
• 原理分析
  • Chain of Thought Empowers Transformers to Solve Inherently Serial Problems ⭐
  • 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
  • Inductive or Deductive? Rethinking the Fundamental Reasoning Abilities of LLMs
  • Beyond Chain-of-Thought: A Survey of Chain-of-X Paradigms for LLMs
  • To CoT or not to CoT? Chain-of-thought helps mainly on math and symbolic reasoning ⭐
  • Why think step by step? Reasoning emerges from the locality of experience
  • Internal Consistency and Self-Feedback in Large Language Models: A Survey ⭐
  • Iteration Head: A Mechanistic Study of Chain-of-Thought ⭐
  • The Impact of Reasoning Step Length on Large Language Models ⭐
  • Do Large Language Models Perform Latent Multi-Hop Reasoning without Exploiting Shortcuts?
  • Compressed Chain of Thought: Efficient Reasoning Through Dense Representations
  • Do LLMs Really Think Step-by-step In Implicit Reasoning?
• 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
  • Distilling System 2 into System 1
• COT样本自动构建/选择
  • AutoCOT：AUTOMATIC CHAIN OF THOUGHT PROMPTING IN LARGE LANGUAGE MODELS
  • Active Prompting with Chain-of-Thought for Large Language Models
  • COMPLEXITY-BASED PROMPTING FOR MULTI-STEP REASONING
• COT能力学习
  • Large Language Models Can Self-Improve
  • Training Chain-of-Thought via Latent-Variable Inference
  • Quiet-STaR: Language Models Can Teach Themselves to Think Before Speaking
  • STaR: Self-Taught Reasoner Bootstrapping ReasoningWith Reasoning
  • V-STaR: Training Verifiers for Self-Taught Reasoners
  • THINK BEFORE YOU SPEAK: TRAINING LANGUAGE MODELS WITH PAUSE TOKENS
  • SELF-DIRECTED SYNTHETIC DIALOGUES AND REVISIONS TECHNICAL REPORT
• 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

• Darwin Godel Machine: Open-Ended Evolution of Self-Improving Agents
• Alpha Evolve
• Can Large Reasoning Models Self-Train
• Unsupervised Post-Training for Multi-Modal LLM Reasoning via GRPO

• 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
  • BOND: Aligning LLMs with Best-of-N Distillation
  • RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math Reasoning by Eight-Fold
  • Generative Verifiers: Reward Modeling as Next-Token Prediction
  • Training Language Models to Self-Correct via Reinforcement Learning
• 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
  • LLM Critics Help Catch LLM Bugs ⭐
  • PROVER-VERIFIER GAMES IMPROVE LEGIBILITY OF LLM OUTPUTS
  • Rule Based Rewards for Language Model Safety
  • Self-critiquing models for assisting human evaluators
• Anthropic
  • A General Language Assistant as a Laboratory for Alignmen
  • Measuring Progress on Scalable Oversight or Large Language Models
  • 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
  • A Minimaximalist Approach to Reinforcement Learning from Human Feedback
  • PANDA: Preference Adaptation for Enhancing Domain-Specific Abilities of LLMs
  • Weak-to-Strong Search: Align Large Language Models via Searching over Small Language Models
  • Weak-to-Strong Extrapolation Expedites Alignment
  • Is DPO Superior to PPO for LLM Alignment? A Comprehensive Study
  • Token-level Direct Preference Optimization
  • SimPO: Simple Preference Optimization with a Reference-Free Reward
  • AUTODETECT: Towards a Unified Framework for Automated Weakness Detection in Large Language Models
  • META-REWARDING LANGUAGE MODELS: Self-Improving Alignment with LLM-as-a-Meta-Judge
  • HELPSTEER: Multi-attribute Helpfulness Dataset for STEERLM
  • Recursive Introspection: Teaching Language Model Agents How to Self-Improve
  • Enhancing Multi-Step Reasoning Abilities of Language Models through Direct Q-Function Optimization
  • DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models
  • GLoRe: When, Where, and How to Improve LLM Reasoning via Global and Local Refinements
  • REFT: Reasoning with REinforced Fine-Tuning
  • SCPO：SELF-CONSISTENCY PREFERENCE OPTIMIZATION
  • MONA: Myopic Optimization with Non-myopic Approval Can Mitigate Multi-step Reward Hacking
  • Optimizing Test-Time Compute via Meta Reinforcement Fine-Tuning
• 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
  • Language Models Resist Alignment
  • Towards a Unified View of Preference Learning for Large Language Models: A Survey

• Inference Scaling
  • An Empirical Analysis of Compute-Optimal Inference for Problem-Solving with Language Models
  • Are More LM Calls All You Need? Towards the Scaling Properties of Compound AI Systems
  • Large Language Monkeys: Scaling Inference Compute with Repeated Sampling
  • Scaling LLM Test-Time Compute Optimally can be More Effective than Scaling Model Parameters ⭐
  • Q*: Improving Multi-step Reasoning for LLMs with Deliberative Planning
  • Planning In Natural Language Improves LLM Search For Code Generation
  • ReST-MCTS∗ : LLM Self-Training via Process Reward Guided Tree Search
  • AlphaZero-Like Tree-Search can Guide Large Language Model Decoding and Training
  • Smaller, Weaker, Yet Better: Training LLM Reasoners via Compute-Optimal Sampling
  • The Surprising Effectiveness of Test-Time Training for Abstract Reasoning
  • Inference Scaling for Long-Context Retrieval Augmented Generation
  • Toward Self-Improvement of LLMs via Imagination, Searching, and Criticizing
  • InfAlign: Inference-aware language model alignment
  • Scaling up Test-Time Compute with Latent Reasoning: A Recurrent Depth Approach
  • What type of inference is planning?
  • Goedel-Prover: A Frontier Model for Open-Source Automated Theorem Proving
• slow thinking COT
  • O1 Replication Journey: A Strategic Progress Report – Part 1 ⭐
  • Marco-o1: Towards Open Reasoning Models for Open-Ended Solutions
  • A Comparative Study on Reasoning Patterns of OpenAI's o1 Model
  • Imitate, Explore, and Self-Improve: A Reproduction Report on Slow-thinking Reasoning Systems
  • Dualformer: Controllable Fast and Slow Thinking by Learning with Randomized Reasoning Traces
  • Training Large Language Models to Reason in a Continuous Latent Space
  • Beyond A∗ : Better Planning with Transformers via Search Dynamics Bootstrapping
  • o1-Coder: an o1 Replication for Coding
  • Scaling of Search and Learning: A Roadmap to Reproduce o1 from Reinforcement Learning Perspective
  • Sky-T1: Train your own O1 preview model within $450
  • Towards System 2 Reasoning in LLMs: Learning How to Think With Meta Chain-of-Thought
  • rStar-Math: Small LLMs Can Master Math Reasoning with Self-Evolved Deep Thinking ⭐
  • Demystifying Long Chain-of-Thought Reasoning in LLMs
  • Towards Large Reasoning Models: A Survey of Reinforced Reasoning with Large Language Models
  • Huggingface Open R1
  • CODEI/O: Condensing Reasoning Patterns via Code Input-Output Prediction
  • Training Language Models to Reason Efficiently
  • s1: Simple test-time scaling
  • Inner Thinking Transformer: Leveraging Dynamic Depth Scaling to Foster Adaptive Internal Thinking
• O3 Related
  • Competitive Programming with Large Reasoning Models
• Memorize at Test Time
  • Titans: Learning to Memorize at Test Time
  • Learning to Reason from Feedback at Test-Time
• RL COT原理
  • SFT Memorizes, RL Generalizes: A Comparative Study of Foundation Model Post-training
  • Cognitive Behaviors that Enable Self-Improving Reasoners, or, Four Habits of Highly Effective STaRs
  • Thoughts Are All Over the Place: On the Underthinking of o1-Like LLMs
  • All Roads Lead to Likelihood: The Value of Reinforcement Learning in Fine-Tuning
• R1 Reprodce
  • LogicRL: Logic-RL: Unleashing LLM Reasoning with Rule-Based Reinforcement Learning
  • SimpleR1
  • Huggingface Open R1
  • DianJin-R1: Evaluating and Enhancing Financial Reasoning in Large Language Models
  • Think Only When You Need with Large Hybrid-Reasoning Models
• RL Agent
  • RAGEN: Understanding Self-Evolution in LLM Agents via Multi-Turn Reinforcement Learning
  • ToolRL: Reward is All Tool Learning Needs
  • ReTool: Reinforcement Learning for Strategic Tool Use in LLMs
  • ReSearch: Learning to Reason with Search for LLMs via Reinforcement Learning
  • Improving Multi-Turn Tool Use with Reinforcement Learning
  • WebThinker: Empowering Large Reasoning Models with Deep Research Capability
• 经验学习
  • Welcome to the Era of Experience
• RL 其他训练方式
  • QWENLONG-L1: Towards Long-Context Large Reasoning Models with Reinforcement Learning
  • REWARDBENCH 2: Advancing Reward Model Evaluation

脱离上文长度这个狭窄的视角重新看待模型记忆

• A-MEM: Agentic Memory for LLM Agents
• MemInsight: Autonomous Memory Augmentation for LLM Agents

• 近期我们也陷入多轮对话优化，发现了角色混乱、理解下降等很多问题

• LLMS GET LOST IN MULTI-TURN CONVERSATION

• 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
  • The Instruction Hierarchy: Training LLMs to Prioritize Privileged Instructions
  • Magpie: Alignment Data Synthesis from Scratch by Prompting Aligned LLMs with Nothing
• 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
  • MAGPIE: Alignment Data Synthesis from Scratch by Prompting Aligned LLMs with Nothing
  • Scaling Synthetic Data Creation with 1,000,000,000 Personas
  • UNLEASHING REASONING CAPABILITY OF LLMS VIA SCALABLE QUESTION SYNTHESIS FROM SCRATCH
  • A Survey on Data Synthesis and Augmentation for Large Language Models
  • AgentInstruct: Toward Generative Teaching with Agentic Flows
  • Unveiling the Flaws: Exploring Imperfections in Synthetic Data and Mitigation Strategies for 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
  • Fine tuning LLMs for Enterprise: Practical Guidelines and Recommendations
• 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

• AGENT AI: SURVEYING THE HORIZONS OF MULTIMODAL INTERACTION
• 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
  • LLMCompiler An LLM Compiler for Parallel Function Calling
  • Re-Invoke: Tool Invocation Rewriting for Zero-Shot Tool Retrieval
• 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
  • ToolGen: Unified Tool Retrieval and Calling via Generation
• 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
    • TaskWeaver: A Code-First Agent Framework
    • Automated Social Science: Language Models as Scientist and Subjects
    • Data Interpreter: An LLM Agent For Data Science
  • 金融
    • WeaverBird: Empowering Financial Decision-Making with Large Language Model, Knowledge Base, and Search Engine
    • FinGPT: Open-Source Financial Large Language Models
    • 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
    • FinAgent： 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
    • ENHANCING ANOMALY DETECTION IN FINANCIAL MARKETS WITH AN LLM-BASED MULTI-AGENT FRAMEWORK
    • TRADINGGPT: MULTI-AGENT SYSTEM WITH LAYERED MEMORY AND DISTINCT CHARACTERS FOR ENHANCED FINANCIAL TRADING PERFORMANCE
    • FinRobot: An Open-Source AI Agent Platform for Financial Applications using Large Language Models
    • LLMFactor: Extracting Profitable Factors through Prompts for Explainable Stock Movement Prediction
    • Alpha-GPT: Human-AI Interactive Alpha Mining for Quantitative Investment
    • Advancing Anomaly Detection: Non-Semantic Financial Data Encoding with LLMs
    • TradExpert: Revolutionizing Trading with Mixture of Expert LLMs
    • FinVision: A Multi-Agent Framework for Stock Market Prediction
    • AI in Investment Analysis: LLMs for Equity Stock Ratings
    • AAPM: Large Language Model Agent-based Asset Pricing Models
    • FinCon: A Synthesized LLM Multi-Agent System with Conceptual Verbal Reinforcement for Enhanced Financial Decision Making
    • TradingAgents: Multi-Agents LLM Financial Trading Framework
    • Pretrained LLM Adapted with LoRA as a Decision Transformer for Offline RL in Quantitative Trading
    • FinRL-DeepSeek: LLM-Infused Risk-Sensitive Reinforcement Learning for Trading Agents
    • FinSphere: A Conversational Stock Analysis Agent Equipped with Quantitative Tools based on Real-Time Database
    • FLAG-Trader: Fusion LLM-Agent with Gradient-based Reinforcement Learning for Financial Trading
    • Ploutos: Towards interpretable stock movement prediction with financial large language model
    • HedgeAgents: A Balanced-aware Multi-agent Financial Trading System
    • TIMERAG: BOOSTING LLM TIME SERIES FORECASTING VIA RETRIEVAL-AUGMENTED GENERATION
    • CausalStock: Deep End-to-end Causal Discovery for News-driven Stock Movement Prediction
  • 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
    • Agent Hospital: A Simulacrum of Hospital with Evolvable Medical Agents
    • Integrating Chemistry Knowledge in Large Language Models via Prompt Engineering
    • CHEMAGENT: SELF-UPDATING LIBRARY IN LARGE LANGUAGE MODELS IMPROVES CHEMICAL REASONING
  • web/mobile Agent
    • AutoWebGLM: Bootstrap And Reinforce A Large Language Model-based Web Navigating Agent
    • A Real-World WebAgent with Planning, Long Context Understanding, and Program Synthesis
    • Mind2Web: Towards a Generalist Agent for the Web
    • MiniWoB++ Reinforcement Learning on Web Interfaces Using Workflow-Guided Exploration
    • WEBARENA: A REALISTIC WEB ENVIRONMENT FORBUILDING AUTONOMOUS AGENTS
    • AutoCrawler: A Progressive Understanding Web Agent for Web Crawler Generation
    • WebLINX: Real-World Website Navigation with Multi-Turn Dialogue
    • WebVoyager: Building an End-to-end Web Agent with Large Multimodal Models
    • CogAgent: A Visual Language Model for GUI Agents
    • Mobile-Agent-v2: Mobile Device Operation Assistant with Effective Navigation via Multi-Agent Collaboration
    • WebCanvas: Benchmarking Web Agents in Online Environments
    • The Dawn of GUI Agent: A Preliminary Case Study with Claude 3.5 Computer Use
    • UI-TARS: Pioneering Automated GUI Interaction with Native Agents
    • Exposing Limitations of Language Model Agents in Sequential-Task Compositions on the Web
  • software engineer
  • Agents in Software Engineering: Survey, Landscape, and Vision
  • ChatDev: Communicative Agents for Software Development
  • Research Agent
    • PaSa: An LLM Agent for Comprehensive Academic Paper Search
    • ResearchAgent: Iterative Research Idea Generation over Scientific Literature with Large Language Models
    • Agent Laboratory: Using LLM Agents as Research Assistants
    • Automated Hypothesis Validation with Agentic Sequential Falsifications
    • Towards an AI co-scientist
  • 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
    • SCIAGENTS: AUTOMATING SCIENTIFIC DISCOVERY THROUGH MULTI-AGENT INTELLIGENT GRAPH REASONING
• 评估
  • Evaluating Verifiability in Generative Search Engines
  • 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
  • Agent-as-a-Judge: Evaluate Agents with Agents
• MultiAgent
  • GENERATIVE AGENTS
  • LET MODELS SPEAK CIPHERS: MULTIAGENT DEBATE THROUGH EMBEDDINGS
  • War and Peace (WarAgent): Large Language Model-based Multi-Agent Simulation of World Wars
  • Small LLMs Are Weak Tool Learners: A Multi-LLM Agent
  • Merge, Ensemble, and Cooperate! A Survey on Collaborative Strategies in the Era of Large Language Models
  • Generative Agents: Interactive Simulacra of Human Behavior ⭐
  • AgentVerse: Facilitating Multi-Agent Collaboration and Exploring Emergent Behaviors in Agents
  • System-1.x: Learning to Balance Fast and Slow Planning with Language Models
  • Agents Thinking Fast and Slow:A Talker-Reasoner Architecture
  • Generative Agent Simulations of 1,000 People
  • Advanced Reasoning and Learning for Autonomous AI Agents
  • Multi-Agent Design: Optimizing Agents with Better Prompts and Topologies
  • 多智能体系统
    • Internet of Agents: Weaving a Web of Heterogeneous Agents for Collaborative Intelligence
    • MULTI-AGENT COLLABORATION: HARNESSING THE POWER OF INTELLIGENT LLM AGENTS
    • Magentic-One: A Generalist Multi-Agent System for Solving Complex Tasks
• 任务型智能体协作
  • METAAGENTS: SIMULATING INTERACTIONS OF HUMAN BEHAVIORS FOR LLM-BASED TASK-ORIENTED COORDINATION VIA COLLABORATIVE
  • 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 ⭐
  • MedAgents: Large Language Models as Collaborators for Zero-shot Medical Reasoning
  • METAGPT: META PROGRAMMING FOR A MULTI-AGENT COLLABORATIVE FRAMEWORK
• 智能体路由
  • One Agent To Rule Them All: Towards Multi-agent Conversational AI
  • A Multi-Agent Conversational Recommender System
• 基座模型路由&Ensemble
  • Large Language Model Routing with Benchmark Datasets
  • LLM-BL E N D E R: Ensembling Large Language Models with Pairwise Ranking and Generative Fusion
  • RouteLLM: Learning to Route LLMs with Preference Data
  • More Agents Is All You Need
  • Routing to the Expert: Efficient Reward-guided Ensemble of Large Language Models
• 自主学习和探索进化
  • 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
  • OS-COPILOT: TOWARDS GENERALIST COMPUTER AGENTS WITH SELF-IMPROVEMENT
  • LLAMA RIDER: SPURRING LARGE LANGUAGE MODELS TO EXPLORE THE OPEN WORLD
  • PAST AS A GUIDE: LEVERAGING RETROSPECTIVE LEARNING FOR PYTHON CODE COMPLETION
  • AutoGuide: Automated Generation and Selection of State-Aware Guidelines for Large Language Model Agents
  • A Survey on Self-Evolution of Large Language Models
  • ExpeL: LLM Agents Are Experiential Learners
  • ReAct Meets ActRe: When Language Agents Enjoy Training Data Autonomy
  • PROACTIVE AGENT: SHIFTING LLM AGENTS FROM REACTIVE RESPONSES TO ACTIVE ASSISTANCE
  • From Novice to Expert: LLM Agent Policy Optimization via Step-wise Reinforcement Learning
  • AGILE: A Novel Reinforcement Learning Framework of LLM Agents
  • Agent Q: Advanced Reasoning and Learning for Autonomous AI Agents
  • ARMAP: SCALING AUTONOMOUS AGENTS VIA AUTOMATIC REWARD MODELING AND PLANNING
  • Search-R1: Training LLMs to Reason and Leverage Search Engines with Reinforcement Learning
• MCP
  • SCALEMCP: DYNAMIC AND AUTO-SYNCHRONIZING MODEL CONTEXT PROTOCOL TOOLS 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
• Custom Agent
  • Creating General User Models from Computer Use

• 经典论文
  • 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 ⭐
  • RETA-LLM: A Retrieval-Augmented Large Language Model Toolkit
  • Atlas: Few-shot Learning with Retrieval Augmented Language Models
  • RRAML: Reinforced Retrieval Augmented Machine Learning
  • FRESHLLMS: REFRESHING LARGE LANGUAGE MODELS WITH SEARCH ENGINE AUGMENTATION
• 微调
  • RLCF：Aligning the Capabilities of Large Language Models with the Context of Information Retrieval via Contrastive Feedback
  • RA-DIT: RETRIEVAL-AUGMENTED DUAL INSTRUCTION TUNING
  • CHAIN-OF-NOTE: ENHANCING ROBUSTNESS IN RETRIEVAL-AUGMENTED LANGUAGE MODELS
  • RAFT: Adapting Language Model to Domain Specific RAG
  • Rich Knowledge Sources Bring Complex Knowledge Conflicts: Recalibrating Models to Reflect Conflicting Evidence
• 其他论文
  • Investigating the Factual Knowledge Boundary of Large Language Models with Retrieval Augmentation
  • PDFTriage: Question Answering over Long, Structured Documents
  • Walking Down the Memory Maze: Beyond Context Limit through Interactive Reading ⭐
  • 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
  • DORIS-MAE: Scientific Document Retrieval using Multi-level Aspect-based Queries
  • Factuality Enhanced Language Models for Open-Ended Text Generation
  • KwaiAgents: Generalized Information-seeking Agent System with Large Language Models
  • Complex Claim Verification with Evidence Retrieved in the Wild
  • Retrieval-Augmented Generation for Large Language Models: A Survey
  • 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
  • T-RAG: Lessons from the LLM Trenches
  • ARAGOG: Advanced RAG Output Grading
  • ActiveRAG: Revealing the Treasures of Knowledge via Active Learning
  • OpenResearcher: Unleashing AI for Accelerated Scientific Research
  • Contextual.ai-RAG2.0
  • Mindful-RAG: A Study of Points of Failure in Retrieval Augmented Generation
  • Memory3 : Language Modeling with Explicit Memory
• 优化检索
  • IAG: Induction-Augmented Generation Framework for Answering Reasoning Questions
  • HyDE：Precise Zero-Shot Dense Retrieval without Relevance Labels
  • PROMPTAGATOR : FEW-SHOT DENSE RETRIEVAL FROM 8 EXAMPLES
  • Query Rewriting for Retrieval-Augmented Large Language Models
  • Query2doc: Query Expansion with Large Language Models ⭐
  • Query Expansion by Prompting Large Language Models ⭐
  • Anthropic Contextual Retrieval
  • Multi-Level Querying using A Knowledge Pyramid
  • A Survey of Query Optimization in Large Language Models
• Ranking
  • A Setwise Approach for Effective and Highly Efficient Zero-shot Ranking with Large Language Models
  • RankVicuna: Zero-Shot Listwise Document Reranking with Open-Source Large Language Models
  • Improving Passage Retrieval with Zero-Shot Question Generation
  • Large Language Models are Effective Text Rankers with Pairwise Ranking Prompting
  • RankRAG: Unifying Context Ranking with Retrieval-Augmented Generation in LLMs
  • Ranking Manipulation for Conversational Search Engines
  • Is ChatGPT Good at Search? Investigating Large Language Models as Re-Ranking Agents
  • Opensource Large Language Models are Strong Zero-shot Query Likelihood Models for Document Ranking
  • T2Ranking: A large-scale Chinese Benchmark for Passage Ranking
  • Learning to Filter Context for Retrieval-Augmented Generation
• 传统搜索方案
  • ASK THE RIGHT QUESTIONS:ACTIVE QUESTION REFORMULATION WITH REINFORCEMENT LEARNING
  • Query Expansion Techniques for Information Retrieval a Survey
  • Learning to Rewrite Queries
  • Managing Diversity in Airbnb Search
• 新向量模型用于Recall和Ranking
  • Augmented Embeddings for Custom Retrievals
  • 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
  • D2LLM: Decomposed and Distilled Large Language Models for Semantic Search
  • Piccolo2: General Text Embedding with Multi-task Hybrid Loss Training
• 优化推理结果
  • Speculative RAG: Enhancing Retrieval Augmented Generation through Drafting
• 动态RAG（When to Search & Search Plan）
  • SELF-RAG: LEARNING TO RETRIEVE, GENERATE, AND CRITIQUE THROUGH SELF-REFLECTION ⭐
  • Self-Knowledge Guided Retrieval Augmentation for Large Language Models
  • Self-DC: When to retrieve and When to generate Self Divide-and-Conquer for Compositional Unknown Questions
  • Small Models, Big Insights: Leveraging Slim Proxy Models To Decide When and What to Retrieve for LLMs
  • Adaptive-RAG: Learning to Adapt Retrieval-Augmented Large Language Models through Question Complexity
  • REAPER: Reasoning based Retrieval Planning for Complex RAG Systems
  • When to Retrieve: Teaching LLMs to Utilize Information Retrieval Effectively
  • PlanRAG: A Plan-then-Retrieval Augmented Generation for Generative Large Language Models as Decision Makers
  • ONEGEN: EFFICIENT ONE-PASS UNIFIED GENERATION AND RETRIEVAL FOR LLMS
  • Probing-RAG: Self-Probing to Guide Language Models in Selective Document Retrieval
• Graph RAG
  • GRAPH Retrieval-Augmented Generation: A Survey
  • From Local to Global: A Graph RAG Approach to Query-Focused Summarization
  • GRAG: Graph Retrieval-Augmented Generation
  • GNN-RAG: Graph Neural Retrieval for Large Language Model Reasoning
  • THINK-ON-GRAPH: DEEP AND RESPONSIBLE REASONING OF LARGE LANGUAGE MODEL ON KNOWLEDGE GRAPH
  • LightRAG: Simple and Fast Retrieval-Augmented Generation
  • THINK-ON-GRAPH: DEEP AND RESPONSIBLE REASON- ING OF LARGE LANGUAGE MODEL ON KNOWLEDGE GRAPH
  • StructRAG: Boosting Knowledge Intensive Reasoning of LLMs via Inference-time Hybrid Information Structurization
• Multistep RAG
  • SYNERGISTIC INTERPLAY BETWEEN SEARCH AND LARGE LANGUAGE MODELS FOR INFORMATION RETRIEVAL
  • Interleaving Retrieval with Chain-of-Thought Reasoning for Knowledge-Intensive Multi-Step Questions
  • Enhancing Retrieval-Augmented Large Language Models with Iterative Retrieval-Generation Synergy
  • RAT: Retrieval Augmented Thoughts Elicit Context-Aware Reasoning in Long-Horizon Generation
  • IM-RAG: Multi-Round Retrieval-Augmented Generation Through Learning Inner Monologues
  • 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
  • MindSearch 思·索: Mimicking Human Minds Elicits Deep AI Searcher
  • RQ-RAG: LEARNING TO REFINE QUERIES FOR RETRIEVAL AUGMENTED GENERATION
  • AutoPRM: Automating Procedural Supervision for Multi-Step Reasoning via Controllable Question Decomposition
• Timeline RAG
  • Unfolding the Headline: Iterative Self-Questioning for News Retrieval and Timeline Summarization
• fast rag
  • MINIRAG: TOWARDS EXTREMELY SIMPLE RETRIEVAL-AUGMENTED GENERATION
  • EasyRAG: Efficient Retrieval-Augmented Generation Framework for Automated Network Operations
• memory Rag
  • KBLAM: KNOWLEDGE BASE AUGMENTED LANGUAGE MODEL

• 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
• Set-of-Mark Prompting Unleashes Extraordinary Visual Grounding in GPT-4V
• RePrompt: Automatic Prompt Editing to Refine AI-Generative Art Towards Precise Expressions
• MedPrompt: Can Generalist Foundation Models Outcompete Special-Purpose Tuning? Case Study in Medicine
• DSPy Assertions: Computational Constraints for Self-Refining Language Model Pipelines
• Prompts as Auto-Optimized Training Hyperparameters: Training Best-in-Class IR Models from Scratch with 10 Gold Labels
• In-Context Learning for Extreme Multi-Label Classification
• Optimizing Instructions and Demonstrations for Multi-Stage Language Model Programs
• DSPy: Compiling Declarative Language Model Calls into Self-Improving Pipelines
• CONNECTING LARGE LANGUAGE MODELS WITH EVOLUTIONARY ALGORITHMS YIELDS POWERFUL PROMP OPTIMIZERS
• TextGrad: Automatic "Differentiation" via Text
• Task Facet Learning: A Structured Approach to Prompt Optimization
• LangGPT: Rethinking Structured Reusable Prompt Design Framework for LLMs from the Programming Language
• PAS: Data-Efficient Plug-and-Play Prompt Augmentation System
• Let Me Speak Freely? A Study on the Impact of Format Restrictions on Performance of Large Language Models
• From Pen to Prompt: How Creative Writers Integrate AI into their Writing Practice
• Does Prompt Formatting Have Any Impact on LLM Performance?
• AUTO-DEMO PROMPTING: LEVERAGING GENERATED OUTPUTS AS DEMONSTRATIONS FOR ENHANCED BATCH PROMPTING
• PROMPTBREEDER: SELF-REFERENTIAL SELF-IMPROVEMENT VIA PROMPT EVOLUTION

• survey
  • Table Meets LLM: Can Large Language Models Understand Structured Table Data? A Benchmark and Empirical Study
  • Large Language Models(LLMs) on Tabular Data: Prediction, Generation, and Understanding - A Survey
  • Exploring the Numerical Reasoning Capabilities of Language Models: A Comprehensive Analysis on Tabular Data
• prompt
  • Large Language Models are Versatile Decomposers: Decompose Evidence and Questions for Table-based Reasoning
  • Tab-CoT: Zero-shot Tabular Chain of Thought
  • Chain-of-Table: Evolving Tables in the Reasoning Chain for Table Understanding
• fintuning
  • TableLlama: Towards Open Large Generalist Models for Tables
  • TableLLM: Enabling Tabular Data Manipulation by LLMs in Real Office Usage Scenarios
• multimodal
  • MMC: Advancing Multimodal Chart Understanding with Large-scale Instruction Tuning
  • ChartLlama: A Multimodal LLM for Chart Understanding and Generation
  • ChartAssisstant: A Universal Chart Multimodal Language Model via Chart-to-Table Pre-training and Multitask Instruction Tuning
  • ChartInstruct: Instruction Tuning for Chart Comprehension and Reasoning
  • ChartX & ChartVLM: A Versatile Benchmark and Foundation Model for Complicated Chart Reasoning
  • MATCHA : Enhancing Visual Language Pretraining with Math Reasoning and Chart Derendering
  • UniChart: A Universal Vision-language Pretrained Model for Chart Comprehension and Reasoning
  • TinyChart: Efficient Chart Understanding with Visual Token Merging and Program-of-Thoughts Learning
  • Tables as Texts or Images: Evaluating the Table Reasoning Ability of LLMs and MLLMs
  • TableVQA-Bench: A Visual Question Answering Benchmark on Multiple Table Domains
  • TabPedia: Towards Comprehensive Visual Table Understanding with Concept Synergy

• 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
• ALFWORLD: ALIGNING TEXT AND EMBODIED ENVIRONMENTS FOR INTERACTIVE LEARNING
• MINEDOJO: Building Open-Ended Embodied Agents with Internet-Scale Knowledge
• LEGENT: Open Platform for Embodied Agents

• 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
• Zyda: A 1.3T Dataset for Open Language Modeling
• Entropy Law: The Story Behind Data Compression and LLM Performance
• Data, Data Everywhere: A Guide for Pretraining Dataset Construction
• Data curation via joint example selection further accelerates multimodal learning
• IMPROVING PRETRAINING DATA USING PERPLEXITY CORRELATIONS
• AI models collapse when trained on recursively generated data

• 金融
  • BloombergGPT： A Large Language Model for Finance
  • FinVis-GPT: A Multimodal Large Language Model for Financial Chart Analysis
  • CFGPT: Chinese Financial Assistant with Large Language Model
  • CFBenchmark: Chinese Financial Assistant Benchmark for Large Language Model
  • InvestLM: A Large Language Model for Investment using Financial Domain Instruction Tuning
  • BBT-Fin: Comprehensive Construction of Chinese Financial Domain Pre-trained Language Model, Corpus and Benchmark
  • PIXIU: A Large Language Model, Instruction Data and Evaluation Benchmark for Finance
  • The FinBen: An Holistic Financial Benchmark for Large Language Models
  • XuanYuan 2.0: A Large Chinese Financial Chat Model with Hundreds of Billions Parameters
  • Towards Trustworthy Large Language Models in Industry Domains
  • When AI Meets Finance (StockAgent): Large Language Model-based Stock Trading in Simulated Real-world Environments
  • A Survey of Large Language Models for Financial Applications: Progress, Prospects and Challenges
• 生物医疗
  • MedGPT: Medical Concept Prediction from Clinical Narratives
  • BioGPT：Generative Pre-trained Transformer for Biomedical Text Generation and Mining
  • PubMed GPT: A Domain-specific large language model for biomedical text ⭐
  • ChatDoctor：Medical Chat Model Fine-tuned on LLaMA Model using Medical Domain Knowledge
  • Med-PaLM：Large Language Models Encode Clinical Knowledge[V1,V2] ⭐
  • SMILE: Single-turn to Multi-turn Inclusive Language Expansion via ChatGPT for Mental Health Support
  • Zhongjing: Enhancing the Chinese Medical Capabilities of Large Language Model through Expert Feedback and Real-world Multi-turn Dialogue
• 其他
  • Galactia：A Large Language Model for Science
  • Augmented Large Language Models with Parametric Knowledge Guiding
  • ChatLaw Open-Source Legal Large Language Model ⭐
  • MediaGPT : A Large Language Model For Chinese Media
  • KITLM: Domain-Specific Knowledge InTegration into Language Models for Question Answering
  • EcomGPT: Instruction-tuning Large Language Models with Chain-of-Task Tasks for E-commerce
  • TableGPT: Towards Unifying Tables, Nature Language and Commands into One GPT
  • LLEMMA: AN OPEN LANGUAGE MODEL FOR MATHEMATICS
  • MEDITAB: SCALING MEDICAL TABULAR DATA PREDICTORS VIA DATA CONSOLIDATION, ENRICHMENT, AND REFINEMENT
  • PLLaMa: An Open-source Large Language Model for Plant Science
  • ADAPTING LARGE LANGUAGE MODELS VIA READING COMPREHENSION

• 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
  • Native Sparse Attention: Hardware-Aligned and Natively Trainable Sparse Attention
• 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
  • PCToolkit: A Unified Plug-and-Play Prompt Compression Toolkit of Large Language Models
  • Are Long-LLMs A Necessity For Long-Context Tasks?
• 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
  • MEGALODON: Efficient LLM Pretraining and Inference with Unlimited Context Length
  • Make Your LLM Fully Utilize the Context
  • Untie the Knots: An Efficient Data Augmentation Strategy for Long-Context Pre-Training in Language Models
  • LIFT: Improving Long Context Understanding of Large Language Models through Long Input Fine-Tuning
• 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
• 评估
  • NOLIMA: Long-Context Evaluation Beyond Literal Matching

• 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
• Into the Unknown Unknowns: Engaged Human Learning through Participation in Language Model Agent Conversations
• Beyond Outlining: Heterogeneous Recursive Planning for Adaptive Long-form Writing with 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
  • Tool-Assisted Agent on SQL Inspection and Refinement in Real-World Scenarios
• 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
• AI Coders Are Among Us: Rethinking Programming Language Grammar Towards Efficient Code Generation

• 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
  • LARGE LANGUAGE MODELS CANNOT SELF-CORRECT REASONING YET
  • Training Language Models to Self-Correct via Reinforcement Learning

• 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
• 通用评估
  • G-EVAL: NLG Evaluation using GPT-4 with Better Human Alignment

• 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
• You Only Cache Once: Decoder-Decoder Architectures for Language Models
• XGrammar: Flexible and Efficient Structured Generation Engine for Large Language Models
• Precise Length Control in Large Language Models
• Top-nσ: Not All Logits Are You Need
• context cache
• Prompt Cache: Modular Attention Reuse for Low-Latency Inference
• SGLang: Efficient Execution of Structured Language Model Programs
• Efficient Prompt Caching via Embedding Similarity
• ChunkAttention: Efficient Self-Attention with Prefix-Aware KV Cache and Two-Phase Partition
• Hydragen: High-Throughput LLM Inference with Shared Prefixes
• Efficient Memory Management for Large Language Model Serving with PagedAttention

• 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
• Automata-based constraints for language model decoding
• SGLang: Efficient Execution of Structured Language Model Programs

• 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
• Arcee's MergeKit: A Toolkit for Merging Large Language Models

• Tricks for Training Sparse Translation Models
• ST-MoE: Designing Stable and Transferable Sparse Expert Models
• Switch Transformers: Scaling to Trillion Parameter Models with Simple and Efficient Sparsity
• GLaM: Efficient Scaling of Language Models with Mixture-of-Experts
• GShard: Scaling Giant Models with Conditional Computation and Automatic Sharding
• OUTRAGEOUSLY LARGE NEURAL NETWORKS: THE SPARSELY-GATED MIXTURE-OF-EXPERTS LAYER
• DeepSpeed-MoE: Advancing Mixture-of-Experts Inference and Training to Power Next-Generation AI Scale
• Dense-to-Sparse Gate for Mixture-of-Experts
• Efficient Large Scale Language Modeling with Mixtures of Experts

• 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
• PreFLMR: Scaling Up Fine-Grained Late-Interaction Multi-modal Retrievers
• Many-Shot In-Context Learning in Multimodal Foundation Models
• Adding Conditional Control to Text-to-Image Diffusion Models
• Ferret-UI: Grounded Mobile UI Understanding with Multimodal LLMs
• ShowUI: One Vision-Language-Action Model for GUI Visual Agent

• 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
  • FUNCTION VECTORS IN LARGE LANGUAGE MODELS
• 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?
  • Language is primarily a tool for communication rather than thought
• 领域能力
  • Capabilities of GPT-4 on Medical Challenge Problems
  • Can Generalist Foundation Models Outcompete Special-Purpose Tuning? Case Study in Medicine
• Understanding LLM Embeddings for Regression

• 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
  • DoRA: Weight-Decomposed Low-Rank Adaptation
• Representation Tuning
• ReFT: Representation Finetuning for Language 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

• 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

• 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