NLP converts human language from raw text data to structured data (computer-understandable), but first, it must perceive the data based on grammar, context, and intent and entities through a process known as Natural Language Understanding (NLU). Natural Language Generation (NLG), on the other hand, converts computer-generated data into human-readable text.

A Large Language Model (LLM) is a machine learning model capable of handling a wide range of Natural Language Processing (NLP) use cases. They are self-supervised, pre-trained foundational models that can be fine-tuned to a wide range of natural language tasks that would previously have required a separate network model. This brings us one step closer to understanding the extraordinary versatility of human language. Following minimal priming, GPT-3 and, more recently, LaMDA can converse with humans on a variety of topics. These models convert text documents into vector embeddings. These dense text embeddings can then be used for a number of tasks, preserving more semantic and syntactic information on words, leading to improved performance in almost every imaginable NLP task.

Large language models converting text into vector embeddings

As we try to answer what LLM is in this blog, we’ll also demystify LLMs by outlining the broad categories of use cases where they can be applied.

Language Models are at the heart of modernNLP, and their applications include speech-to-text, sentiment analysis, text summarization, spell checking, and token classification. Google Search Bar’s autocomplete feature or the conversational abilities of Amazon’s Alexa or Apple’s Siri that we all use is powered by LLM. These models can determine the probability of the next token in most NLP tasks by analyzing the given text. Unigrams, N-grams, Exponential, or Neural Networks can be used to represent the Language Model.

The Timeline

Each year, Large Language models get larger, more powerful, and cheaper to train. RNN models were the cutting-edge NLP language models up until 2017. These are helpful for sequential tasks like machine translation, general natural language generation, and abstractive summarization. RNN models process words sequentially in context. Word2Vec and GloVe are two new methodologies for acquiring vector representations of words that use neural networks or unsupervised learning during this time. Both Word2vec and GloVe are of the primary models that allow us to represent a word as a vector embedding while also capturing the semantic meaning of the text. They are the two most popular algorithms for word embeddings that bring out the semantic similarity of words by capturing different aspects of a word’s meaning.

But these models struggle to parallelize as a result, and they perform poorly at maintaining contextual relationships across long text inputs.

The Era of Transformers

The Transformer is a novel architecture in NLP that aims to solve sequence-to-sequence tasks while easily handling long-range dependencies.
It is based on an encoder-decoder structure, but it does not use recurrence or convolutions to generate output. Introduced in 2017, it quickly demonstrated effective results when modeling data with long-term dependencies. Transformers were originally designed to solve NLP tasks, but their application has grown to achieve incredible results in a variety of disciplines.

Recent developments in NLP have been in the works for a few years, beginning 2018 with the introduction of two enormous deep learning models: BERT and GPT (Generative Pre-Training) by Open AI ( Bidirectional Encoder Representations from Transformers ).

Generative Pre-trained Transformer (GPT) models developed by OpenAI require a small amount of input text to generate large volumes of relevant and sophisticated outputs. GPT models, unlike BERT, are unidirectional; the main advantage is their amount of pre-trained data.

BERT was proposed in 2018 with an architecture similar to GPT. As the name implies, one of its primary differences from GPT is its bidirectionality, helping to better understand the context, giving it a significant advantage over other models. BERT achieved state-of-the-art performance in tasks such as question answering and text classification by releasing a base and a (extremely) larger model, and it can be used for a variety of other NLP tasks simply by fine-tuning with a much smaller task-specific corpus.

GPT-3, by OpenAI remains to be one of the most important AI language models ever developed. This research paper describes in great detail the features, performance, limitations and risk of the GPT-3 model. GPT-3 leverages the transformer architecture and is ingested with huge amounts of data from diverse sources which created a sort of general-purpose tool.

It is an autoregressive language model with 175 billion parameters. These “Few-Shot Learners” language models not only scaled but also greatly improved performance, achieving parity with prior state-of-the-art fine-tuning approaches.

The Multi-Modality to Multi-Modality Multitask Mega-transformer (M6) model announced by Alibaba in May 2021 was described on ArXiv in a paper containing 10 billion parameters pre-trained on 1.9TB of images and 292GB of Chinese language text. Meta AI is joining this trend to democratize access to LLMs by sharing OPT-175B, an exceptionally large model with 175 billion parameters trained on publicly available datasets.

Microsoft and NVIDIA have recently released the Megatron-Turing Natural Language Generation (MT-NLG), boasting an excess of 530 billion parameters. It is the largest model developed to date for reading comprehension and natural language inference.

LLM Model size comparison with years


  1. Misinformation. Due to the fluency of these models, experts have expressed concern about the mass production of false information using them; people may mistakenly believe that these models have produced the output. Essays, tweets, and news articles can be falsely or misleadingly produced using models like GPT-3. Participants, nevertheless, questioned whether hiring people to produce such propaganda is simpler, more cost-effective, or more effective.
  2. Training AI systems has significant environmental and financial costs. The use of power-hungry GPUs for machine learning training has already been blamed for increased CO2 emissions.
  3. Bias. Studies have revealed that these LLM models contain bias (racial, gender, and religious) and discriminatory ideas). In 2015,  Amazon’s AI specialists uncovered a very big problem in its AI recruitment tool for its sexist bias against women. Another example is when researchers discovered racial bias in an algorithm used on more than 200 million U.S. citizens in 2019 to identify patients who require additional medical attention. White people were heavily favored by the model.
  4. Economic impacts. Increasing the model’s accessibility via the API could serve as a means of gathering data regarding the economic effects of model usage on people. It would reveal new applications for the model in automation.


These large language models are the next frontier for artificial intelligence. This is an exciting time because any developer or team can now tackle some of the most difficult NLP challenges by leveraging cutting-edge AI technologies made available through simple API calls. powers a leading AI platform for enterprises to develop and deploy deep learning object tracking applications with a single end-to-end solution. Get in touch with our team at: Contact Us