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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