Types Of LLM

The scalability of large language models is remarkable. Answering queries, summarizing documents, translating languages, and completing sentences are all activities that a single model can handle. The content generation process, as well as the use of search engines and virtual assistants, could be significantly impacted by LLMs.

What Are the Best Large Language Models?

Some of the best and most widely used Large Language Models are as follows –

  • Open AI
  • ChatGPT
  • GPT-3
  • GooseAI
  • Claude
  • Cohere
  • GPT-4

Types of Large Language Models

To meet the many demands and difficulties of natural language processing (NLP), various kinds of large language models have been created. We can examine a few of the most prominent kinds.

Read: How to Incorporate Generative AI Into Your Marketing Technology Stack

1. Autoregressive language models

To generate text, autoregressive models use a sequence of words to predict the following word. Models like GPT-3 are examples of this. The goal of training autoregressive models is to increase the probability that they will generate the correct next word given a certain context. Their strength is in producing coherent and culturally appropriate content, but they have a tendency to generate irrelevant or repetitive responses and can be computationally expensive.

Example: GPT-3

2. Transformer-based models

Big language models often make use of transformers, a form of deep learning architecture. An integral part of numerous LLMs is the transformer model, which was first proposed by Vaswani et al. in 2017. Thanks to its transformer architecture, the model can efficiently process and generate text while capturing contextual information and long-range dependencies.

Example: Roberta (Robustly Optimized BERT Pretraining Approach) by Facebook AI

3. Encoder-decoder models

Machine translation, summarization, and question answering are some of the most popular applications of encoder-decoder models. The two primary parts of these models are the encoder and the decoder. The encoder reads and processes the input sequence, while the decoder generates the output sequence. The encoder is trained to convert the input data into a representation with a fixed length, which is then utilized by the decoder to produce the output sequence. A model that uses an encoder-decoder design is the “Transformer,” which is based on transformers.

Example: MarianMT (Marian Neural Machine Translation) by the University of Edinburgh

4. Pre-trained and fine-tuned models

Because they have been pre-trained on massive datasets, many large language models have a general understanding of language patterns and semantics. Using smaller datasets tailored to each job or domain, these pre-trained models can subsequently be fine-tuned. Through fine-tuning, the model might become highly proficient in a certain job, such as sentiment analysis or named entity identification. When compared to the alternative of training a huge model from the beginning for every task, this method saves both computational resources and time.

Example: ELECTRA (Efficiently Learning an Encoder that Classifies Token Replacements Accurately)

5. Multilingual models

A multilingual model can process and generate text in more than one language. These models are trained using text in various languages. Machine translation, multilingual chatbots, and cross-lingual information retrieval are among the applications that could benefit from them. Translating knowledge from one language to another is made possible by multilingual models that take advantage of shared representations across languages.

Example: XLM (Cross-lingual Language Model) developed by Facebook AI Research

6. Hybrid models

To boost performance, hybrid models incorporate the best features of many architectures. Some models may include recurrent neural networks (RNNs) in addition to transformer-based architectures. When processing data sequentially, RNNs are another popular choice of neural network. They can be incorporated into LLMs to capture not just the self-attention processes of transformers but also sequential dependencies.

Example: UniLM (Unified Language Model) is a hybrid LLM that integrates both autoregressive and sequence-to-sequence modeling approaches

Many more kinds of huge language models have been created; these are only a handful of them. When it comes to the difficulties of comprehending and generating natural language, researchers and engineers are always looking for new ways to improve these models’ capabilities.


When it comes to processing language, large language model (LLM) APIs are going to be game-changers. Using algorithms for deep learning and machine learning, LLM APIs give users unparalleled access to NLP capabilities. These new application programming interfaces (APIs) allow programmers to build apps with unprecedented text interpretation and response capabilities.

LLMs come in various types, each tailored to specific tasks and applications. These include autoregressive models like GPT and BERT-based models like T5, which excel in text generation, comprehension, translation, and more. Understanding the distinctions among these models is crucial for deploying them effectively in diverse language processing tasks.

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