安庆网站建设价格,公众号开发者密码是什么意思,青岛市住房和城乡建设局,个人网页制作方案论文1#xff1a; ChatGPTs One-year Anniversary: Are Open-Source Large Language Models Catching up?
简介
2022年11月#xff0c;OpenAI发布了ChatGPT#xff0c;这一事件在AI社区甚至全世界引起了轰动。首次#xff0c;一个基于应用的AI聊天机器人能够提供有帮助、…论文1 ChatGPTs One-year Anniversary: Are Open-Source Large Language Models Catching up?
简介
2022年11月OpenAI发布了ChatGPT这一事件在AI社区甚至全世界引起了轰动。首次一个基于应用的AI聊天机器人能够提供有帮助、安全和有用的答案遵循人类指令甚至承认并纠正之前的错误。作为第一个这样的应用ChatGPT在其推出仅两个月内用户数量就达到了1亿远远快于其他流行应用如TikTok或YouTube。因此它也吸引了巨额的商业投资因为它有望降低劳动成本自动化工作流程甚至为客户带来新的体验。
但ChatGPT的闭源特性可能引发诸多问题。首先由于不了解内部细节比如预训练和微调过程很难正确评估其潜在风险尤其是考虑到大模型可能生成有害、不道德和虚假的内容。其次有报道称ChatGPT的性能随时间变化妨碍了可重复的结果。第三ChatGPT经历了多次故障仅在2023年11月就发生了两次重大故障期间无法访问ChatGPT网站及其API。最后采用ChatGPT的企业可能会关注API调用的高成本、服务中断、数据所有权和隐私问题以及其他不可预测的事件比如最近有关CEO Sam Altman被解雇并最终回归的董事会闹剧。
此时开源大模型应运而生社区一直在积极推动将高性能的大模型保持开源。然而截至2023年末大家还普遍认为类似Llama-2或Falcon这样的开源大模型在性能上落后于它们的闭源模型如OpenAI的GPT3.5ChatGPT和GPT-4Anthropic的Claude2或Google的Bard3其中GPT-4通常被认为是最出色的。然而令人鼓舞的是差距正在变得越来越小开源大模型正在迅速赶上。 地址[2311.16989] ChatGPTs One-year Anniversary: Are Open-Source Large Language Models Catching up? (arxiv.org)
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 Instructionshttps://arxiv.org/pdf/2304.12244.pdf
幻觉 Siren’s Song in the AI Ocean: A Survey on Hallucination in Large Language Modelshttps://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 learninghttps://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 ImageBind: One Embedding Space To Bind Them All https://arxiv.org/abs/2305.05665 Meta-Transformer: A Unified Framework for Multimodal Learning https://arxiv.org/abs/2307.10802
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 Synthesishttps://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 Generationhttps://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?idyzkSU5zdwD 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/2112.06905 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/2309.11235 RWKV: Reinventing RNNs for the Transformer Era https://arxiv.org/abs/2305.13048 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
