Overview of Large Language Models

Large language models (LLMs) are deep learning models that can perform various natural language processing (NLP) and natural language generation (NLG) tasks. They are trained on massive amounts of text data, such as books, news articles, web pages, and social media posts, to learn the patterns and structures of natural language.

However, LLMs are not always able to perform well on specific tasks or domains that require specialized knowledge or vocabulary. For example, a general LLM may not be able to accurately answer questions about medical terms or legal documents. To overcome this limitation, LLMs can be fine-tuned on smaller datasets that are relevant to the target task or domain. Fine-tuning involves updating the weights of the pre-trained LLM on a new dataset, allowing the model to adapt to the specific context and improve its performance.

In this blog post, we will compare some of the most popular base LLMs and their fine-tuned versions.

Base LLMs

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