Large Language Model (LLM) Fine-Tuning Algorithm

Context:
- It can adjust the weights and parameters of the LLM based on a task-specific dataset to improve its performance on that particular task.
- It can incorporate techniques like transfer learning, where the pre-trained model is utilized as a starting point and further fine-tuned for the target task.
- It can leverage backpropagation and gradient descent to compute the gradients and update the model's parameters during the fine-tuning process.
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Example(s):
- a BERT fine-tuning algorithm, for BERT (Bidirectional Encoder Representations from Transformers) model, such as [[]].
- a GPT fine-tuning algorithm, for GPT (Generative Pre-trained Transformer) models, such as LoRA algorithm.
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Counter-Example(s):
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See: Machine Learning, Large Language Model, Fine-Tuning, BERT Model, GPT Model.

References

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- LLM fine-tuning algorithm is a procedure designed to modify Large Language Models (LLMs) to be proficient in specific tasks or fields. This is achieved by further training these models using smaller, dedicated datasets relating to the desired task, thereby enhancing their aptitude and performance in natural language processing tasks. This technique bolsters the ability of businesses to realize high performance on particular tasks cost-effectively, using less data and computational resources than necessary to train a model from the ground up. The process entails altering the foundational pre-trained model in the preparation phase before refining it with the task-specific dataset. The decision to fine-tune a model is subject to the goals and prerequisites of the task or domain. Fine-tuning contributes to improved precision, adaptability to specific tasks, and the customization of pre-trained models. It is, however, met with challenges including data accessibility issues, privacy concerns