In-context learning is a way to use ChatGPT without having to fine-tune it. This is done by providing ChatGPT with a few examples of the desired output, in the form of prompts and completions. ChatGPT will then use these examples to learn how to generate text in the desired style or format. To use ChatGPT with in-context learning, simply provide it with a few examples of the desired output. For example, if you want ChatGPT to write poems, you could provide it with a few examples of poems. Or, if you want ChatGPT to write code, you could provide it with a few examples of code snippets. Once ChatGPT has learned from the examples, you can then start giving it prompts to generate new text. For example, you could give ChatGPT the prompt “Write a poem about a cat” and it would generate a poem about a cat.
Full fine tuning is a more involved process that requires you to provide ChatGPT with a large dataset of text and code. This dataset should be representative of the tasks that you want ChatGPT to perform. To fine-tune ChatGPT, you can use the OpenAI Playground. Once you have created an account and logged in, you can click on the “Fine-tune” button to start the process. The fine tuning process will take some time to complete, depending on the size of your dataset. Once the fine tuning process is complete, you will be able to use ChatGPT to perform the tasks that you trained it on.
Here are some tips for using ChatGPT with in-context learning versus full fine tuning:
In-context learning is a good option if you don’t have a large dataset of text and code. It is also a good option if you need ChatGPT to perform a specific task that is not well-represented by the pre-trained model. It is a technique to achieve good model performance without full fine tuning by using only few-shot prompts, in which a small number of examples of solved tasks are provided as part of the input to the trained model. If the number of examples provided are 1, then it is called zero shot coding. When the examples provided are 5, then it is called 5 shot prompting and so on.
Full fine tuning is a good option if you have a large dataset of text and code and you need ChatGPT to perform a variety of tasks. Full fine tuning can also be used to improve the performance of ChatGPT on a specific task.
See Also: Incontext learning overview