Fine tuning large language models (LLMs) with instruction is a technique for improving the performance of LLMs on specific tasks by training them on a small dataset of examples that explicitly specify the desired behavior. This is in contrast to pre-training LLMs on massive datasets of text and code, which gives them a general understanding of the world but does not necessarily teach them how to follow specific instructions.
To fine-tune an LLM with instruction, you first need to create a dataset of examples that include both the instruction and the desired output. For example, if you want to fine-tune an LLM to translate English to French, you would create a dataset of examples that include the English sentence and the corresponding French translation. Once you have created your dataset, you can fine-tune the LLM using a variety of machine learning techniques. One common approach is to use supervised learning, where the LLM is trained to predict the desired output for each instruction in the dataset.
Another approach is to use reinforcement learning, where the LLM is rewarded for generating outputs that match the desired output. Both supervised and reinforcement learning can be used to fine-tune LLMs with instruction.
Fine tuning LLMs with instruction has been shown to be effective for a wide range of tasks, including translation, summarization, question answering, and code generation. It is a powerful technique for improving the performance of LLMs on specific tasks, and it is becoming increasingly popular as LLMs are used in more and more applications.
See Also: Incontext Learning