AI Agent 与大模型经典论文推荐

本文整理了一份 AI Agent 与大模型领域的经典论文清单，涵盖多个核心方向，可作为研究参考。

更有趣的 AI Agent

• Generative Agents: Interactive Simulacra of Human Behavior https://arxiv.org/abs/2304.03442
• RoleLLM: Benchmarking, Eliciting, and Enhancing Role-Playing Abilities of Large Language Models https://arxiv.org/abs/2310.00746
• Role play with large language models https://www.nature.com/articles/s41586-023-06647-8
• Exploring Large Language Models for Communication Games: An Empirical Study on Werewolf https://arxiv.org/abs/2309.04658
• MemGPT: Towards LLMs as Operating Systems https://arxiv.org/abs/2310.08560
• Augmenting Language Models with Long-Term Memory https://arxiv.org/abs/2306.07174
• Do LLMs Possess a Personality? Making the MBTI Test an Amazing Evaluation for Large Language Models https://arxiv.org/pdf/2307.16180.pdf

更有用的 AI Agent

• The Rise and Potential of Large Language Model Based Agents: A Survey https://arxiv.org/abs/2309.07864
• MetaGPT: Meta Programming for A Multi-Agent Collaborative Framework https://arxiv.org/abs/2308.00352
• Communicative Agents for Software Development https://arxiv.org/pdf/2307.07924.pdf
• Large Language Models Can Self-Improve https://arxiv.org/abs/2210.11610
• Evaluating Human-Language Model Interaction https://arxiv.org/abs/2212.09746
• Large Language Models can Learn Rules https://arxiv.org/abs/2310.07064
• AgentBench: Evaluating LLMs as Agents https://arxiv.org/abs/2308.03688
• WebArena: A Realistic Web Environment for Building Autonomous Agents https://arxiv.org/abs/2307.13854
• TableGPT: Towards Unifying Tables, Nature Language and Commands into One GPT https://arxiv.org/abs/2307.08674

任务规划与分解

• Chain-of-Thought Prompting Elicits Reasoning in Large Language Models https://arxiv.org/abs/2201.11903
• Tree of Thoughts: Deliberate Problem Solving with Large Language Models https://arxiv.org/abs/2305.10601
• Implicit Chain of Thought Reasoning via Knowledge Distillation https://arxiv.org/abs/2311.01460
• ReAct: Synergizing Reasoning and Acting in Language Models https://arxiv.org/abs/2210.03629
• ART: Automatic multi-step reasoning and tool-use for large language models https://arxiv.org/abs/2303.09014
• Branch-Solve-Merge Improves Large Language Model Evaluation and Generation https://arxiv.org/abs/2310.15123
• WizardLM: Empowering Large Language Models to Follow Complex Instructions https://arxiv.org/pdf/2304.12244.pdf

幻觉

• Siren's Song in the AI Ocean: A Survey on Hallucination in Large Language Models https://arxiv.org/pdf/2309.01219.pdf
• Check Your Facts and Try Again: Improving Large Language Models with External Knowledge and Automated Feedback https://arxiv.org/abs/2302.12813
• SelfCheckGPT: Zero-Resource Black-Box Hallucination Detection for Generative Large Language Models https://arxiv.org/abs/2303.08896
• WebBrain: Learning to Generate Factually Correct Articles for Queries by Grounding on Large Web Corpus https://arxiv.org/abs/2304.04358

多模态

• Learning Transferable Visual Models From Natural Language Supervision (CLIP) https://arxiv.org/abs/2103.00020
• An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale (ViT): https://arxiv.org/abs/2010.11929
• MiniGPT-v2: large language model as a unified interface for vision-language multi-task learning https://arxiv.org/abs/2310.09478
• MiniGPT-4: Enhancing Vision-Language Understanding with Advanced Large Language Models https://arxiv.org/abs/2304.10592
• NExT-GPT: Any-to-Any Multimodal LLM https://arxiv.org/pdf/2309.05519.pdf
• Visual Instruction Tuning (LLaVA) https://arxiv.org/pdf/2304.08485.pdf
• Improved Baselines with Visual Instruction Tuning (LLaVA-1.5) https://arxiv.org/abs/2310.03744
• Sequential Modeling Enables Scalable Learning for Large Vision Models (LVM) https://arxiv.org/pdf/2312.00785.pdf
• CoDi-2: In-Context, Interleaved, and Interactive Any-to-Any Generation https://arxiv.org/pdf/2311.18775.pdf
• Neural Discrete Representation Learning (VQ-VAE) https://browse.arxiv.org/pdf/1711.00937.pdf
• Taming Transformers for High-Resolution Image Synthesis (VQ-GAN) https://arxiv.org/abs/2012.09841
• Swin Transformer: Hierarchical Vision Transformer using Shifted Windows https://arxiv.org/abs/2103.14030
• BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models https://browse.arxiv.org/pdf/2301.12597.pdf
• InstructBLIP: Towards General-purpose Vision-Language Models with Instruction Tuning https://browse.arxiv.org/pdf/2305.06500.pdf

图片/视频生成

• High-Resolution Image Synthesis with Latent Diffusion Models https://arxiv.org/pdf/2112.10752.pdf
• Structure and Content-Guided Video Synthesis with Diffusion Models (RunwayML Gen1) https://browse.arxiv.org/pdf/2302.03011.pdf
• Hierarchical Text-Conditional Image Generation with CLIP Latents (DaLLE-2) https://arxiv.org/pdf/2204.06125.pdf
• AnimateDiff: Animate Your Personalized Text-to-Image Diffusion Models without Specific Tuning https://arxiv.org/abs/2307.04725
• Adding Conditional Control to Text-to-Image Diffusion Models (ControlNet) https://arxiv.org/abs/2302.05543
• SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesis https://arxiv.org/abs/2307.01952
• Zero-1-to-3: Zero-shot One Image to 3D Object https://arxiv.org/abs/2303.11328
• Scaling Vision Transformers to 22 Billion Parameters https://arxiv.org/abs/2302.05442
• Glow: Generative Flow with Invertible 1×1 Convolutions https://browse.arxiv.org/pdf/1807.03039.pdf
• Language Model Beats Diffusion – Tokenizer is Key to Visual Generation https://arxiv.org/pdf/2310.05737.pdf
• InstaFlow: One Step is Enough for High-Quality Diffusion-Based Text-to-Image Generation https://arxiv.org/pdf/2309.06380.pdf
• Perceptual Losses for Real-Time Style Transfer and Super-Resolution https://arxiv.org/pdf/1603.08155.pdf
• CogView: Mastering Text-to-Image Generation via Transformers https://arxiv.org/abs/2105.13290
• Diffusion Models for Video Prediction and Infilling https://arxiv.org/abs/2206.07696

语音合成

• Conditional Variational Autoencoder with Adversarial Learning for End-to-End Text-to-Speech (VITS) https://browse.arxiv.org/pdf/2106.06103.pdf
• Neural Codec Language Models are Zero-Shot Text to Speech Synthesizers (VALL-E) https://arxiv.org/abs/2301.02111
• Speak Foreign Languages with Your Own Voice: Cross-Lingual Neural Codec Language Modeling (VALL-E X) https://arxiv.org/pdf/2303.03926.pdf
• MusicLM: Generating Music From Text https://arxiv.org/abs/2301.11325

大模型基础

• Attention Is All You Need https://arxiv.org/abs/1706.03762
• Sequence to Sequence Learning with Neural Networks https://arxiv.org/abs/1409.3215
• Neural Machine Translation by Jointly Learning to Align and Translate https://arxiv.org/abs/1409.0473
• BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding https://arxiv.org/abs/1810.04805
• Scaling Laws for Neural Language Models https://arxiv.org/pdf/2001.08361.pdf
• Emergent Abilities of Large Language Models https://openreview.net/pdf?id=yzkSU5zdwD
• Training Compute-Optimal Large Language Models (ChinChilla scaling law) https://arxiv.org/abs/2203.15556
• Scaling Instruction-Finetuned Language Models https://arxiv.org/pdf/2210.11416.pdf
• Direct Preference Optimization: Your Language Model is Secretly a Reward Model https://arxiv.org/pdf/2305.18290.pdf
• Progress measures for grokking via mechanistic interpretability https://arxiv.org/abs/2301.05217
• Language Models Represent Space and Time https://arxiv.org/abs/2310.02207
• GLaM: Efficient Scaling of Language Models with Mixture-of-Experts https://arxiv.org/abs/2112.06902
• Adam: A Method for Stochastic Optimization https://arxiv.org/abs/1412.6980
• Efficient Estimation of Word Representations in Vector Space (Word2Vec) https://arxiv.org/abs/1301.3781
• Distributed Representations of Words and Phrases and their Compositionality https://arxiv.org/abs/1310.4546

GPT

• Language Models are Few-Shot Learners (GPT-3) https://arxiv.org/abs/2005.14165
• Language Models are Unsupervised Multitask Learners (GPT-2) https://d4mucfpksywv.cloudfront.net/better-language-models/language_models_are_unsupervised_multitask_learners.pdf
• Improving Language Understanding by Generative Pre-Training (GPT-1) https://s3-us-west-2.amazonaws.com/openai-assets/research-covers/language-unsupervised/language_understanding_paper.pdf
• Training language models to follow instructions with human feedback (InstructGPT) https://arxiv.org/pdf/2203.02155.pdf
• Evaluating Large Language Models Trained on Code https://arxiv.org/pdf/2107.03374.pdf
• Harnessing the Power of LLMs in Practice: A Survey on ChatGPT and Beyond https://arxiv.org/abs/2304.13712
• Instruction Tuning with GPT-4 https://arxiv.org/pdf/2304.03277.pdf
• The Dawn of LMMs: Preliminary Explorations with GPT-4V(ision) https://arxiv.org/abs/2309.17421
• Sparks of Artificial General Intelligence: Early experiments with GPT-4 https://arxiv.org/abs/2303.12712
• Weak-to-Strong Generalization: Eliciting Strong Capabilities With Weak Supervision https://arxiv.org/abs/2312.09390

开源大模型

• LLaMA: Open and Efficient Foundation Language Models https://arxiv.org/abs/2302.13971
• Llama 2: Open Foundation and Fine-Tuned Chat Models https://arxiv.org/pdf/2307.09288.pdf
• Vicuna: An Open-Source Chatbot Impressing GPT-4 with 90%* ChatGPT Quality https://lmsys.org/blog/2023-03-30-vicuna/
• LMSYS-Chat-1M: A Large-Scale Real-World LLM Conversation Dataset https://arxiv.org/abs/2309.11998
• Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena https://arxiv.org/abs/2306.05685
• How Long Can Open-Source LLMs Truly Promise on Context Length? https://lmsys.org/blog/2023-06-29-longchat/
• Mixtral of experts https://mistral.ai/news/mixtral-of-experts/
• OpenChat: Advancing Open-source Language Models with Mixed-Quality Data https://arxiv.org/abs/2309.11230
• RWKV: Reinventing RNNs for the Transformer Era https://arxiv.org/abs/2305.13040
• Mamba: Linear-Time Sequence Modeling with Selective State Spaces https://arxiv.org/ftp/arxiv/papers/2312/2312.00752.pdf
• Retentive Network: A Successor to Transformer for Large Language Models https://arxiv.org/abs/2307.08621
• Baichuan 2: Open Large-scale Language Models https://arxiv.org/abs/2309.10305
• GLM-130B: An Open Bilingual Pre-trained Model https://arxiv.org/abs/2210.02414
• Qwen Technical Report https://arxiv.org/abs/2309.16609
• Skywork: A More Open Bilingual Foundation Model https://arxiv.org/abs/2310.19341

微调

• Learning to summarize from human feedback https://arxiv.org/abs/2009.01325
• Self-Instruct: Aligning Language Model with Self Generated Instruction https://arxiv.org/abs/2212.10560
• Scaling Down to Scale Up: A Guide to Parameter-Efficient Fine-Tuning https://arxiv.org/abs/2303.15647
• LoRA: Low-Rank Adaptation of Large Language Models https://arxiv.org/abs/2106.09685
• Vera: Vector-Based Random Matrix Adapation https://arxiv.org/pdf/2310.11454.pdf
• QLoRA: Efficient Finetuning of Quantized LLMs https://arxiv.org/abs/2305.14314
• Chain of Hindsight Aligns Language Models with Feedback https://arxiv.org/abs/2302.02676
• Beyond Human Data: Scaling Self-Training for Problem-Solving with Language Models https://arxiv.org/pdf/2312.06585.pdf

性能优化

• Efficient Memory Management for Large Language Model Serving with PagedAttention (vLLM) https://arxiv.org/abs/2309.06180
• FlashAttention: Fast and Memory-Efficient Exact Attention with IO-Awareness https://arxiv.org/abs/2205.14135
• S-LoRA: Serving Thousands of Concurrent LoRA Adapters https://arxiv.org/abs/2311.03285
• GPipe: Efficient Training of Giant Neural Networks using Pipeline Parallelism https://proceedings.neurips.cc/paper/2019/file/093f65e080a295f8076b1c5722a46aa2-Paper.pdf
• Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism https://arxiv.org/pdf/1909.08053.pdf
• ZeRO: Memory Optimizations Toward Training Trillion Parameter Models https://arxiv.org/pdf/1910.02054.pdf