Merge pull request #50720 from MicrosoftDocs/rmcmurray-patch-1

Fixing noun/verb agreement

Author: prmerger-automator[bot]

learn-pr/wwl-data-ai/fundamentals-generative-ai/includes/3b-use-language-models.md

@@ -5,7 +5,7 @@ In general, language models can be considered in two categories: *Large Language
 
 |Large Language Models (LLMs)|Small Language Models (SLMs)|
 |-|-|
-|LLMs are trained with vast quantities of text that represents a wide range of general subject matter – typically by sourcing data from the Internet and other generally available publications.| SLMs are trained with smaller, more subject-focused datasets|
+|LLMs are trained with vast quantities of text that represent a wide range of general subject matter – typically by sourcing data from the Internet and other generally available publications.| SLMs are trained with smaller, more subject-focused datasets|
 |When trained, LLMs have many billions (even trillions) of parameters (weights that can be applied to vector embeddings to calculate predicted token sequences).|Typically have fewer parameters than LLMs.|
 |Able to exhibit comprehensive language generation capabilities in a wide range of conversational contexts.|This focused vocabulary makes them effective in specific conversational topics, but less effective at more general language generation.|
 |Their large size can impact their performance and make them difficult to deploy locally on devices and computers.|The smaller size of SLMs can provide more options for deployment, including local deployment to devices and on-premises computers; and makes them faster and easier to fine-tune.|