{"id":5469,"date":"2024-02-10T13:48:33","date_gmt":"2024-02-10T13:48:33","guid":{"rendered":"https:\/\/processtalks.com\/?p=5469"},"modified":"2024-02-12T15:10:52","modified_gmt":"2024-02-12T15:10:52","slug":"customizing-llms-for-your-unique-needs","status":"publish","type":"post","link":"https:\/\/processtalks.com\/en\/customizing-llms-for-your-unique-needs\/","title":{"rendered":"Customizing LLMs for Your Unique Needs"},"content":{"rendered":"\n
In the fast-paced field of Artificial Intelligence (AI), the last year has witnessed an unprecedented surge in the use of Generative AI, particularly through the widespread adoption of Large Language Models (LLMs) for a myriad of tasks. From summarisation or text rewriting functions to more intricate jobs, LLMs have become the go-to solution for various challenges in the AI field.<\/p>\n\n\n\n
To truly harness their power, however, there is often the need to customize and ground these models to meet specific requirements. Some techniques for doing so are prompting<\/strong>, fine-tuning<\/strong> and retrieval augmented generation (RAG)<\/strong>. Prompting and fine-tuning can be seen as differing from RAG in that they involve teaching the model on the “how” (i.e., how to better perform a task), whereas RAG involves teaching the model on the “what” (what knowledge it must have in order to understand and answer on a domain). In this article, we’ll focus on the former two techniques, shedding light on how they can elevate your AI projects to new heights.<\/p>\n\n\n\n Prompting<\/strong> involves providing explicit instructions or examples to guide the language model’s output. Essentially, you’re instructing the model on how to perform a task or create specific content.\u00a0 It is optimal when the task requires a more generalized approach, and you want to guide the model’s behavior without delving into highly specific details.<\/p>\n\n\n\n By contrast, fine-tuning<\/strong> is a more targeted approach where the model is trained on a particular dataset. It involves adapting a pre-trained model to a more specialized domain or set of tasks, and is the best option when the task at hand requires a deep understanding of domain knowledge or adaptation to a particular industry. For fine-tuning, large collections of data are needed to cover a relevant range of variety and therefore avoid model overfitting<\/em>, i.e., when the model learns training data too well but fails on new unseen data due to being different from the learnt data.<\/p>\n\n\n\n The following table details key differences among the two approaches:<\/p>\n\n\n\n <\/p>\n\n\n\n We will explore the implications of using these two approaches taking as a use case a project on deploying a natural language interface<\/strong> that allows users to interact with their mobile app effortlessly, solely through voice commands. Note that this is not just about convenience; it’s a step towards inclusivity and accessibility for all.<\/p>\n\n\n\n<\/a>Prompting vs. Fine-tuning<\/h1>\n\n\n\n
Prompting<\/strong><\/td> Fine-tuning<\/strong><\/td><\/tr> Scope<\/strong><\/strong><\/td><\/tr> Generally involves providing broad instructions and\/or examples to guide the model’s behavior without significant modifications to its pre-existing knowledge.<\/td> Involves specific training on a larger dataset, adapting the model to a more specialized context or domain.<\/td><\/tr> Flexibility<\/strong><\/strong><\/td><\/tr> Offers a more flexible and generalized approach, suitable for a wide range of tasks.<\/td> Provides a more tailored and domain-specific adaptation, sacrificing some generalizability for enhanced performance in a specific context.<\/td><\/tr> Complexity<\/strong><\/td><\/tr> Less complex as it typically involves working with the existing capabilities of the pre-trained model.<\/td> More complex, requiring the creation and curation of a dataset specific to the target task or domain.<\/td><\/tr><\/tbody><\/table> <\/a>Target Application<\/h1>\n\n\n\n