<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>

CC BY-SA 4.0
2023-08-04T09:55:45.003Z
Are you pretraining or fine-tuning?
Alexander Wan
CC BY-SA 4.0
2023-08-04T11:18:10.090Z
Fine-tuning a pretrained model to get better on a task.
Peyman
<natural-language-processing><gpt><large-language-models><language-model><fine-tuning>
Optimal Quantity of Training Data for Fine-Tuning an LLM: Is Bigger Always Better?
Peyman
# 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

