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Introduction to large language model

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Introduction

In recent news you heard of that there are many open source models announced by big tech companies like Google, Facebook, Microsoft, openai, huggingface, etc. These models are very large in size and trained on large amount of data. These models are called large language models. Examples of these models are GPT-3, GPT-2, BERT, T5, etc.

These models are trained on large amount of data and can be used for many NLP tasks like text generation, text classification, text summarization, question answering, etc.

What we will learn in this article

  • What is large language model(LLM) and it's defination.
  • How does actually LLM works?
  • What are benefits of LLM and What we can achieve using LLM?
  • What will be the challenges using and building LLM?
  • What will be future of LLM?
  • Example of LLM like GPT-3, GPT-2, BERT, T5, etc.

What is LLM?

Large language model is a large deep learning model which is trained on large amount of data. Data can be anything like text, images, videos, etc to learn the pattern and predict the next word, next sentence, next image, next video, etc based on some prompt.

These can be pre-trained on large amount of data and then fine-tuned on specific task such as text generation, text classification, text summarization, question answering, etc.

How does LLM works?

LLM works on the principle of self-supervised learning. Self-supervised learning is a type of unsupervised learning where we don't need to label the data. We just need to provide some prompt to the model and model will predict the next word, next sentence, next image, next video, etc based on the prompt.

Below is an example of how self-supervised learning works for LLM models:

  • First we train the model on large amount of data. For example, we train the model on large amount of text data using large deep learning model like transformer.
  • For example we can train model on large amount of novel books.
  • After training the model, we can give it a prompt like "Once upon a time there was a king and queen" and it will predict the next word like "lived in a castle".

LLMs can also be used to predict the next sentence in a paragraph, the next image in a video, or even the next song in a playlist.

Benefits of LLM

  • LLM can be used for many NLP tasks like text generation, text classification, text summarization, question answering, etc. They also can be multimodel.
  • Improve the accuracy of variety of NLP tasks.
  • Can automate tasks that are time consuming and expensive.
  • Improve communication between peoples by translating languages or generating text that are easy to understand.

Challenges of LLM

  • LLM models are very large in size and require large amount of data to train.
  • Also they require large amount of compute power to train and inference.
  • LLMs can be baised and can be used for bad purposes like generating fake news, fake images, etc.

Despite these challenges, LLMs are very useful and can be used for many NLP tasks. In recent months LLMs have revolutionized the NLP field and many new models are coming every month.

Future of LLM

LLMs models like GPT-3, BERT, etc are played a major role in changing the working and thinking of progrmmers, coders, researchers, etc. Examples of such LLM are ChatGPT, Google Bard, etc.

Below are some applications of LLMs:

  • Virtual Assistants: Virtual assistants like Siri, Alexa, Google Assistant, etc can be improved using LLMs. They can be used to generate more human like responses.
  • Chatbots: Chatbots can be improved using LLMs. They can be used to generate more human like responses. Like GPT-3 chatbot and Bard chatbot.
  • Text Generation: LLMs can be used to generate text like GPT-3, GPT-2, etc.
  • Text Summarization: LLMs can be used to summarize the text like T5, BART, etc.
  • Medical Research: LLMs can be used to generate new drugs, new molecules, etc that will help in new treatments of diseases.
  • Legal Research: LLMs can be used to generate legal documents, legal contracts, etc.
  • Code Generation: LLMs can be used to generate code like GPT-3, Codex, etc.
  • Education: LLMs can be used to generate educational content like GPT-3, etc.

There are more applications on which LLMs can be used.

Examples of LLMs

There are many LLMs available in open source. Some of them are:

  • GPT-3: GPT-3 is a large language model developed by OpenAI. It is trained on large amount of data and can be used for many NLP tasks like text generation, text classification, text summarization, question answering, etc.

  • Google BERT(Bidirectional Encoder Representations from Transformers): BERT is developed by Google. It can understand questions and generate answers.

  • T5(Text-to-Text Transfer Transformer): Developed by Google. It is trained on a variety of language tasks and can perform text-to-text transformations, like translating text to another language, creating a summary, and question answering.

  • Hugging face:Hugging face is an organization that provides many open source Large language models(LLMs).

Conclusion

In this article we learned about Large language models(LLMs). We learned about what is LLM, how does LLM works, benefits of LLM, challenges of LLM, future of LLM, examples of LLM, etc.

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