NLP还能做什么？北航等多机构百页论文，系统阐述后ChatGPT技术链

　　原标题：NLP还能做什么？北航等多机构百页论文，系统阐述后ChatGPT技术链

　　Training language models to follow instructions with human feedback [7]

　　Generative Agents: Interactive Simulacra of Human Behavior, https://arxiv.org/pdf/2304.03442.pdf

　　Inner Monologue [33]

　　1.Experience Grounds Language, https://arxiv.org/abs/2004.10151

　　2.Tool Learning with Foundation Models

　　3.Foundation Models for Decision Making: Problems, Methods, and Opportunities

　　4.ChatGPT for Robotics: Design Principles and Model Abilities

　　5.Augmented Language Models: a Survey

　　6.Sparks of Artificial General Intelligence: Early experiments with GPT-4

　　7.Training language models to follow instructions with human feedback, https://arxiv.org/abs/2203.02155

　　8.Conversational AI, http://coai.cs.tsinghua.edu.cn/

　　9.AI Chains: Transparent and Controllable Human-AI Interaction by Chaining Large Language Model Prompts, https://arxiv.org/abs/2110.01691

　　10.Interactive Text Generation

　　11.Evaluating Human-Language Model Interaction

　　12.Transformer Memory as a Differentiable Search Index, https://arxiv.org/abs/2202.06991

　　13.Language Models as Knowledge Bases?, https://arxiv.org/abs/1909.01066

　　14.WebGPT: Browser-assisted question-answering with human feedback, https://arxiv.org/abs/2112.09332

　　15.Atlas:Few-shot Learning withRetrieval Augmented Language Models, https://arxiv.org/pdf/2208.03299.pdf

　　16.MINEDOJO:Building Open-EndedEmbodied Agents with Internet-Scale Knowledge, https://arxiv.org/pdf/2206.08853.pdf

　　17.Chain-of-Thought Prompting Elicits Reasoning in Large Language Models, https://arxiv.org/abs/2201.11903

　　18.ReAct: Synergizing Reasoning and Acting Inlanguage Models, https://arxiv.org/abs/2210.03629

　　19.Least-to-Most Prompting Enables complex reasoning in Large Language Models, https://arxiv.org/pdf/2205.10625.pdf

　　20.Measuring and Narrowingthe Compositionality Gap in Language Models, https://ofir.io/self-ask.pdf

　　21.HuggingGPT, https://arxiv.org/abs/2303.17580

　　22.Toolformer: Language Models Can Teach Themselves to Use Tools, https://arxiv.org/abs/2302.04761

　　23.Socratic Models, https://arxiv.org/pdf/2204.00598.pdf

　　24.MindCraft: Theory of Mind Modeling for Situated Dialogue in Collaborative Tasks, https://aclanthology.org/2021.emnlp-main.85/

　　25.Computational Language Acquisition with Theory of Mind, https://openreview.net/forum?id=C2ulri4duIs

　　26.Generative Agents: Interactive Simulacra of Human Behavior, https://arxiv.org/pdf/2304.03442.pdf

　　27.CAMEL: Communicative Agents for “Mind” Exploration of Large Scale Language Model Society, https://www.camel-ai.org/

　　28.OFA: Unifying Architectures, Tasks, and Modalities Through a Simple Sequence-to-Sequence Learning Framework, https://arxiv.org/abs/2202.03052

　　29.BridgeTower: Building Bridges Between Encoders in Vision-Language Representation Learning, https://arxiv.org/abs/2206.08657

　　30.BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models, https://arxiv.org/pdf/2301.12597.pdf

　　31.Do As I Can,Not As I Say:Grounding Language in Robotic Affordances, https://say-can.github.io/

　　32.Grounded Decoding: Guiding Text Generation with Grounded Models for Robot Control, https://grounded-decoding.github.io/

　　33.Inner Monologue:Embodied Reasoning through Planning with Language Models, https://innermonologue.github.io/

　　Large Language Models with Controllable Working Memory, https://arxiv.org/abs/2211.05110

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