Large Language Model for Dummies — Part 3
Here are the first two parts on the Large Language Model for Dummies series.
Large Language Models for Dummies — Part 1 | by Venkatesh Narayanan | May, 2023 | Medium
Large Language Models for Dummies — Part 2 | by Venkatesh Narayanan | May, 2023 | Medium
As we saw in the previous two articles how Large Language Models are built using Neural Networks and Machine Learning algorithms. Large Language Models is the breakthrough that is powering various Natural Language Processing (NLP) applications and enabling scenarios like question answering, summarization, speech recognition etc.
Large Language Models have gained prominence with the success of ChatGPT.
Applications of Large Language Model
Large Language Models are powering many NLP scenarios and applications. After being trained on large corpus of data, the LLM has the ability to capture the various complexities of the natural language. Once it has captured the complexities of the natural language, it can enable the following scenarios:
Summarization of content
Generation of text based on previous content
Rewriting text
Classification of Data
Extraction of Data
LLMs like ChatGPT3, BERT are capable of supporting all the above scenarios as they are trained on large corpus of text.
Important Use Cases of Large Language Model
Following are the top use cases of LLM:
Language Translation — LLMs can be used to translate between different languages. The models use deep learning algorithms to understand the language structure of source and target languages.
Content Generation — LLMs have made it easy to create coherent and logical content for generating new blog posts, ideas, articles and other forms of content. Based on the extensive data that they have been trained on, they can generate new content that are unique and readable.
Sentiment Analysis — LLMs can detect and classify emotional states and sentiments in labeled text. It can detect emotions and other sentiments that can help with user opinions and reviews.
Comprehension, Summarization of Text — LLMs provide a way to understand text and its content. Due to the nature of LLMs being trained on large corpus of data, they can comprehend, summarize and classify text in different forms and patterns.
Answering Queries — LLMs have enabled interaction with user’s natural language query. The LLMs have made it possible to detect, understand intent and respond in natural language.
LLMs make the above use cases possible as they are trained on large amounts of data that provide efficient results. LLMs use a technique called self-supervised training to improve their performance and accuracy. LLMs have been trained to understand the intricate patterns in data.
Conclusion
Large Language Models is about to revolutionize the Natural Language Processing and enables accurate language understanding. The effectiveness of the LLMs is dependent on the quality of data that they have been trained on.