Updates to small and large LM article

Author: schaffererin

articles/aks/concepts-ai-ml-language-models.md

@@ -17,7 +17,7 @@ Language models are powerful machine learning models used for natural language p
 
 *Conventional language models* have been used in supervised settings for research purposes where the models are trained on well-labeled text datasets for specific tasks. *Pre-trained language models* offer an accessible way to get started with AI and have become more widely used in recent years. These models are trained on large-scale text corpora from the internet using deep neural networks and can be fine-tuned on smaller datasets for specific tasks.
 
-The size of a language model is determined by the its number of parameters, or *weights*, that determine how the model processes input data and generates output. Parameters are learned during the training process by adjusting the weights within layers of the model to minimize the difference between the model's predictions and the actual data. The more parameters a model has, the more complex and expressive it is, but also the more computationally expensive it is to train and use.
+The size of a language model is determined by its number of parameters, or *weights*, that determine how the model processes input data and generates output. Parameters are learned during the training process by adjusting the weights within layers of the model to minimize the difference between the model's predictions and the actual data. The more parameters a model has, the more complex and expressive it is, but also the more computationally expensive it is to train and use.
 
 In general, **small language models** have *fewer than 10 billion parameters*, and **large language models** have *more than 10 billion parameters*. For example, the new Microsoft Phi-3 model family has three versions with different sizes: mini (3.8 billion parameters), small (7 billion parameters), and medium (14 billion parameters).
 
@@ -37,7 +37,7 @@ Small language models are a good choice if you want models that are:
 Small language models are suitable for use cases that require:
 
 * **Limited data or resources**, and you need a quick and simple solution.
-* **Well-defined or narrow tasks**, and you don't need a lot of creativity in the output.
+* **Well-defined or narrow tasks**, and you don't need much creativity in the output.
 * **High-precision and low-recall tasks**, and you value accuracy and quality over coverage and quantity.
 * **Sensitive or regulated tasks**, and you need to ensure the transparency and accountability of the model.