In recent years, there has been a significant development in large language models, thanks to the advancements in artificial intelligence and natural language processing. These language models have become instrumental in applications such as text generation, translation, and conversation understanding. In this article, we will explore 12 of the best large language models in the AI domain.

1. GPT-3 (OpenAI): Perhaps the most famous large language model, GPT-3 has shown remarkable capabilities in generating human-like text. With 175 billion parameters, this generative pre-trained transformer can complete text tasks like writing essays, programming code, and answering questions.

2. BERT (Google): Bidirectional Encoder Representations from Transformers is a model designed for pre-training deep bidirectional representations from unlabeled text. It has achieved outstanding results on several NLP tasks such as sentiment analysis and question-answering.

3. RoBERTa (Facebook): RoBERTa is an optimized version of BERT that uses more data and improves upon its training method. It has demonstrated better performance on downstream NLP tasks compared to BERT.

4. T5 (Google): Text-to-Text Transfer Transformer is an encoder-decoder architecture model designed for sequence-to-sequence tasks. It has excelled in translation, summarization, and question answering tasks using a unifying text-to-text approach.

5. XLNet (Google/CMU): A language model designed to make use of permutation-based training and bidirectional context, XLNet outperforms BERT on numerous benchmarks while requiring less pre-processing.

6. Megatron (NVIDIA): Developed by NVIDIA, Megatron is a massive transformer-based language model that scales up to 8 billion parameters using model parallelism. It can be used for several NLP tasks such as translation and summarization.

7. ELECTRA (Google): The Efficiently Learning an Encoder that can be used as a Transformer or Classifier model incorporates a new pre-training technique that is computationally more efficient than BERT and GPT and achieves state-of-the-art results.

8. ALBERT (Google Research): A Lite BERT, ALBERT, uses parameter-reduction techniques to enable better scaling with large language models. It has achieved similar performance to BERT while having fewer parameters.

9. ERNIE (Baidu): Enhanced Representation through kNowledge IntEgration, ERNIE is a deep learning model for NLP tasks that incorporates knowledge graphs for better reasoning and understanding of context.

10. DistilBERT (Hugging Face): As a lighter version of BERT, DistilBERT keeps 95% of the original model’s performance using only 50% of its parameters, making it an ideal choice for applications where resources are limited.

11. CTRL (Salesforce): As a conditional transformer language model, CTRL enables users to control various aspects of generated text by providing control codes such as text style, topic, and sentiment in input strings.

12. GShard (Google): An innovative approach in large-scale deep learning, GShard focuses on spatial partitioning to scale the number of parameters in a language model effectively. It demonstrated remarkable performance on machine translation tasks with billions of parameters.

These large language models have made remarkable strides in understanding and generating human-like text, demonstrating the potential for further advancements in AI-powered language applications. As these models continue to evolve, we can expect novel applications and transformations across numerous industries.