GPT-3, GPT-4, ChatGPT, GPT-J, and generative models in general, are very powerful AI models. We're showing you here how to effectively use these models thanks to few-shot learning, also known as prompt engineering.

cross-posted from: https://lemmy.world/post/699214

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NLP Cloud (Natural Language Processing Cloud)

GPT-3, GPT-4, ChatGPT, GPT-J, and generative models in general, are very powerful AI models. We’re showing you here how to effectively use these models thanks to few-shot learning, also known as prompt engineering. Few-shot learning is like training/fine-tuning an AI model, by simply giving a couple of examples in your prompt.

GPT-3, GPT-4, And ChatGPT

GPT-3, GPT-4, and ChatGPT, released by OpenAI, are the most powerful AI model ever released for text understanding and text generation.

GPT-3 was trained on 175 billion parameters, which makes it extremely versatile and able to understanding pretty much anything! We do not know the number of parameters in GPT-4 but results are even more impressive.

You can do all sorts of things with these generative models like chatbots, content creation, entity extraction, classification, summarization, and much more. But it takes some practice and using them correctly might require a bit of work.

GPT-J, GPT-NeoX, And Dolphin

GPT-NeoX and GPT-J are both open-source Natural Language Processing models, created by, a collective of researchers working to open source AI (see EleutherAI’s website).

GPT-J has 6 billion parameters and GPT-NeoX has 20 billion parameters, which makes them the most advanced open-source Natural Language Processing models as of this writing. They are direct alternatives to OpenAI’s proprietary GPT-3 Curie.

These models are very versatile. They can be used for almost any Natural Language Processing use case: text generation, sentiment analysis, classification, machine translation,… and much more (see below). However using them effectively sometimes takes practice. Their response time (latency) might also be longer than more standard Natural Language Processing models.

GPT-J and GPT-NeoX are both available on the NLP Cloud API. On NLP Cloud you can also use Dolphin, an in-house advanced generative model that competes with ChatGPT, GPT-3, and even GPT-4. Below, we’re showing you examples obtained using the GPT-J endpoint of NLP Cloud on GPU, with the Python client. If you want to copy paste the examples, please don’t forget to add your own API token. In order to install the Python client, first run the following: pip install nlpcloud.

Few-Shot Learning

Few-shot learning is about helping a machine learning model make predictions thanks to only a couple of examples. No need to train a new model here: models à la GPT-3 and GPT-4 are so big that they can easily adapt to many contexts without being re-trained.

Giving only a few examples to the model does help it dramatically increase its accuracy.

In Natural Language Processing, the idea is to pass these examples along with your text input. See the examples below!

Also note that, if few-shot learning is not enough, you can also fine-tune GPT-3 on OpenAI’s website and GPT-J and Dolphin on NLP Cloud so the models are perfectly tailored to your use case.

You can easily test few-shot learning on the NLP Cloud Playground, in the text generation section. Click here to try text generation on the Playground. Then simply use one of the examples showed below in this article and see for yourself.

If you use a model that understands natural human instructions like ChatGPT or ChatDolphin, you might not always have to use few-shot learning, but it is alway interesting to apply few-shot learning when possible in order to get the most advanced results. If you do not want to use few-shot learning, read our dedicated guide about how to use ChatGPT and ChatDolphin with simple instructions: see the article here.

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Data Privacy And Security

NLP Cloud is HIPAA / GDPR / CCPA compliant, and working on the SOC 2 certification. We cannot see your data, we do not store your data, and we do not use your data to train our own AI models.

You can read the full page and article here. If you’re still interested, consider checking out this other amazing resource detailing how to utilize chat-gpt alternatives.