PanGu-α Description
PanGu-a was developed under MindSpore, and trained on 2048 Ascend AI processors. The MindSpore Auto-parallel parallelism strategy was implemented to scale the training task efficiently to 2048 processors. This includes data parallelism as well as op-level parallelism. We pretrain PanGu-a with 1.1TB of high-quality Chinese data collected from a variety of domains in order to enhance its generalization ability. We test the generation abilities of PanGua in different scenarios, including text summarizations, question answering, dialog generation, etc. We also investigate the effects of model scaling on the few shot performances across a wide range of Chinese NLP task. The experimental results show that PanGu-a is superior in performing different tasks with zero-shot or few-shot settings.
PanGu-α Alternatives
OPT
The ability of large language models to learn in zero- and few shots, despite being trained for hundreds of thousands or even millions of days, has been remarkable. These models are expensive to replicate, due to their high computational cost. The few models that are available via APIs do not allow access to the full weights of the model, making it difficult to study. Open Pre-trained Transformers is a suite decoder-only pre-trained transforms with parameters ranging from 175B to 125M. We aim to share this fully and responsibly with interested researchers. We show that OPT-175B has a carbon footprint of 1/7th that of GPT-3. We will also release our logbook, which details the infrastructure challenges we encountered, as well as code for experimenting on all of the released model.
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ERNIE 3.0 Titan
Pre-trained models of language have achieved state-of the-art results for various Natural Language Processing (NLP). GPT-3 has demonstrated that scaling up language models pre-trained can further exploit their immense potential. Recently, a framework named ERNIE 3.0 for pre-training large knowledge enhanced models was proposed. This framework trained a model that had 10 billion parameters. ERNIE 3.0 performed better than the current state-of-the art models on a variety of NLP tasks. In order to explore the performance of scaling up ERNIE 3.0, we train a hundred-billion-parameter model called ERNIE 3.0 Titan with up to 260 billion parameters on the PaddlePaddle platform. We also design a self supervised adversarial and a controllable model language loss to make ERNIE Titan generate credible texts.
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ChatGPT
ChatGPT is an OpenAI language model. It can generate human-like responses to a variety prompts, and has been trained on a wide range of internet texts. ChatGPT can be used to perform natural language processing tasks such as conversation, question answering, and text generation.
ChatGPT is a pretrained language model that uses deep-learning algorithms to generate text. It was trained using large amounts of text data. This allows it to respond to a wide variety of prompts with human-like ease. It has a transformer architecture that has been proven to be efficient in many NLP tasks.
ChatGPT can generate text in addition to answering questions, text classification and language translation. This allows developers to create powerful NLP applications that can do specific tasks more accurately.
ChatGPT can also process code and generate it.
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VideoPoet
VideoPoet, a simple modeling technique, can convert any large language model or autoregressive model into a high quality video generator. It is composed of a few components. The autoregressive model learns from video, image, text, and audio modalities in order to predict the next audio or video token in the sequence. The LLM training framework introduces a mixture of multimodal generative objectives, including text to video, text to image, image-to video, video frame continuation and inpainting/outpainting, styled video, and video-to audio. Moreover, these tasks can be combined to provide additional zero-shot capabilities. This simple recipe shows how language models can edit and synthesize videos with a high level of temporal consistency.
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Company Details
Company:
Huawei
Year Founded:
1987
Headquarters:
China
Website:
arxiv.org/abs/2104.12369
Media
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