Body: <p>I am currently working on fine-tuning an LLM for a specific task, and I am trying to determine the optimal size for my training dataset. Intuitively, one might think that the more data, the better. However, I am aware that in some contexts, this may lead to overfitting or other issues.</p>
  <p>What is the general consensus on the optimal quantity of training data required for fine-tuning a Large Language Model? Is bigger always better?</p>
  
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2023-08-04T09:55:45.003Z
Comments.Text: Are you pretraining or fine-tuning?
Comments.UserDisplayName: Alexander Wan
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2023-08-04T11:18:10.090Z
Comments.Text: Fine-tuning a pretrained model to get better on a task.
Comments.UserDisplayName: Peyman
Tags: <natural-language-processing><gpt><large-language-models><language-model><fine-tuning>
Title: Optimal Quantity of Training Data for Fine-Tuning an LLM: Is Bigger Always Better?
UserDisplayName: Peyman
ThreadText: # Optimal Quantity of Training Data for Fine-Tuning an LLM: Is Bigger Always Better?
  
  (Asked by: username_0 on 2023-08-04)
  
  I am currently working on fine-tuning an LLM for a specific task, and I am trying to determine the optimal size for my training dataset. Intuitively, one might think that the more data, the better. However, I am aware that in some contexts, this may lead to overfitting or other issues.
  
  What is the general consensus on the optimal quantity of training data required for fine-tuning a Large Language Model? Is bigger always better?
  
  **Tags:** natural-language-processing, gpt, large-language-models, language-model, fine-tuning
  > **Comments:**
  > **username_1**: Are you pretraining or fine-tuning?  
  > **username_0**: Fine-tuning a pretrained model to get better on a task.  
  
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  ## 0 answers
  
  