LLM- Large Language Models

Large Language Models(LLMs) have generated much hype in recent months (see Figure 1). The demand has led to the ongoing development of websites and solutions that leverage language models. ChatGPT set the record for the fastest-growing user base in January 2023, proving that language models are here to stay. This is also shown by the fact that Bard, Google’s answer to ChatGPT, was introduced in February 2023.

Language models are also opening new possibilities for businesses, as they can:

• Automate process
• Save time and money
• Drive personalization
• Increase accuracy in tasks

Yet, large language models are a new development in computer science. Because of this, business leaders may not be up-to-date on such models. We wrote this article to inform curious business leaders in large language models:

• Definition
• Examples
• Use cases
• Training
• Benefits
• Challenges  

What is a large language model?

Large language models (LLMs) are  foundation models that utilize deep learning in natural language processing(NLP) and natural language generation(NLG) tasks. For the purpose of helping them learn the complexity and linkages of language, large language models are pre-trained on a vast amount of data. Using techniques such as:

• Fine-tuning
• In-context learning

Large language model examples

There are many open-source language models that are deployable on-premise or in a private cloud, which translates to fast business adoption and robust cybersecurity. Some large language models in this category are:

• BLOOM
• NeMO LLM
• XLM-RoBERTa
• XLNet
• Cohere
• GLM-130B

Most of the leading language model developers are based in the US, but there are successful examples from China and Europe as they work to catch up on Generative AI.

What are the use cases of language models?

Large language models can be applied to a variety of use cases and industries, including healthcare, retail, tech, and more. The following are use cases that exist in all industries:

• Text summarization
• Text generation
• Sentiment analysis
• Content creation
• Chatbots, virtual assistant, and conversational AI
• Named entity recognition
• Speech recognition and synthesis
• Image annotation
• Text-to-speech synthesis
• Spell correction
• Machine translation
• Recommendation systems
• Fraud detection
• Code generation

How large language models are trained

Large language models are deep learning neural networks, a subset of artificial intelligence and machine learning. Large language models are first pre-trained so that they learn basic language tasks and functions. Pretraining is the step that requires massive computational power and cutting-edge hardware. 

Pre-training vs. fine-tuning

Once the model is pre-trained, it can be trained with task-specific new data to fine-tune it for specific use cases. The fine-tuning method has high computational efficiency since it requires less data and power, making it a cheaper method.

Benefits of large language models

1- Reduce manual labor and costs

Language models can be used to automate many processes, such as:

• Sentiment analysis
• Customer service 
• Content creation
• Fraud detection
• Prediction and classification 

Automating such tasks leads to reduced manual labor and related costs