We refer to GPT-3 and its successor OpenAI models, including ChatGPT and GPT4, as GPT-3 family large language models (GLLMs). With the ever-rising popularity of GLLMs, especially in the research community, there is a strong need for a comprehensive survey which summarizes the recent ...
ChatGPT for Robotics:在此过程中,ChatGPT作为一个类似于PaLM - E 的高级任务规划器,通过函数调用相应的低级API来生成动作。该过程包括几个步骤: 首先定义一个API列表,如目标检测API、抓取API、移动API等; 其次,为ChatGPT构建提示符,指定环境、API功能、任务目标等; 第三,迭代地促使ChatGPT使用定义的可执行任务的...
作为一种通用的学习范式,ICL可以在各种传统的数据集和基准上进行研究,例如SuperGLUE (Wang et al., 2019), SQuAD (Rajpurkar et al., 2018)。在SuperGLUE上用32个随机采样的例子实现ICL, Brown等人(2020)发现GPT3可以在COPA和ReCoRD上取得与最先进(SOTA)微调性能相当的结果,但在大多数NLU任务上仍落后于微调。Hao...
First survey paper to present a comprehensive review of GLLMs with 350+ papers.Discusses various foundation concepts from transformers to large language models.Presents GPT-3 family large language models in detail.Discusses the performances of GLLMs in various downstream tasks.Presents multiple insightful...
This work focuses on the aspect of facial manipulation in Deepfake, encompassing Face Swapping, Face Reenactment, Talking Face Generation, Face Attribute Editing and Forgery Detection. We believe this will be the most comprehensive survey to date on facial manipulation and detection technologies. Please...
能力增强。由于其强大的能力,GPT-3已成为OpenAI开发更具能力的LLM的基础模型。总体而言,OpenAI已经探索了两种主要方法来进一步改进GPT-3模型,即在代码数据上进行训练和与人类偏好的对齐,具体如下所述。 在代码数据上进行训练。原始的GPT-3模型(在纯文本上进行预训练)的一个主要限制在于缺乏对复杂任务的推理能力,例如...
A Survey on Visual Transformer阅读,以及自己对相关引文的理解。 Transformer 作为NLP领域的大杀器,目前已经在CV领域逐渐展露锋芒,大有替代CNN的趋势,在图像分类,视频处理,low/high level的视觉任务都有相应的transformer刷榜。这篇文章在介绍这些工作的同时,讨论了他们的challenges和今后可能的研究方向。
This repository is for the first comprehensive survey on Meta AI's Segment Anything Model (SAM). - GitHub - djene-mengistu/Awesome-Segment-Anything: This repository is for the first comprehensive survey on Meta AI's Segment Anything Model (SAM).
Generalizing to Unseen Domains: A Survey on Domain Generalization 代码地址:https://github.com/jindongwang/transferlearning/tree/master/code/DeepDG I. Introduction 有许多与泛化相关的研究主题,如领域适应(domain adaptation)、元学习(meta-learning)、迁移学习(transfer learning)、协变量漂移(covariate shift)...
3. Methods 4. Results 5. Discussion 6. Limitations 7. Conclusions Author statement Acknowledgments APPENDIX A: The Survey Instrument APPENDIX B: Participant Demographics and Comments APPENDIX C: Conference Comparison for Extreme Labor Displacement Scenarios References VitaeShow full outline Cited by (59...