𝐓𝐡𝐞 𝐯𝐚𝐫𝐢𝐨𝐮𝐬 𝐋𝐚𝐫𝐠𝐞 𝐋𝐚𝐧𝐠𝐮𝐚𝐠𝐞 𝐌𝐨𝐝𝐞𝐥𝐬 (𝐋𝐋𝐌𝐬) 𝐨𝐟 𝐀𝐈 𝐚𝐧𝐝 𝐭𝐡𝐞𝐢𝐫 𝐟𝐞𝐚𝐭𝐮𝐫𝐞𝐬.

October 2, 2023

An artificial intelligence (AI) algorithm known as a large language model (LLM) employs deep learning methods and extraordinarily big data sets to comprehend, condense, produce, and anticipate new text. The phrase “generative AI” is also closely related to LLMs, which are actually a subset of generative AI designed exclusively to support the creation of text-based content.

What are large language models used for?

LLMs have become increasingly popular because they have broad applicability for a range of NLP tasks, including the following:

  • Sentiment analysis. Most LLMs may be used for sentiment analysis to assist users in better understanding a piece of content’s or a specific response’s intended audience.
  • Rewriting content. Another skill is the capacity to rewrite a passage of text.
  • Conversational AI and chatbots. Compared to earlier generations of AI technologies, LLMs can make it possible to have a discussion with a user that usually feels more natural.
  • Rewriting content. Another skill is the capacity to rewrite a passage of text.
  • Translation. The capacity to translate from one language to another is a characteristic of LLMs who have received multilingual training.
  • Text generation. The major use case is the capacity to generate text on any subject that the LLM has been taught on.
  • Content summary. LLMs can be used to summarize sections of text or pages of text.

These models are designed to understand and generate human-like text based on the vast amount of data they have been trained on. Here are some of the prominent LLMs and their key features:

  1. GPT-3 (Generative Pre-trained Transformer 3):
    • Developed by OpenAI, GPT-3 is one of the largest and most well-known LLMs.
    • It has 175 billion parameters, making it highly capable of understanding and generating text in multiple languages.
    • GPT-3 can be used for various natural language processing tasks, including text generation, translation, question-answering, and more.
  2. BERT (Bidirectional Encoder Representations from Transformers):
    • Developed by Google AI, BERT focuses on understanding the context of words in a sentence.
    • BERT is designed to improve search engine results and can be fine-tuned for specific NLP tasks.
    • It’s known for its bidirectional training, allowing it to capture context and meaning more accurately.
  3. T5 (Text-to-Text Transfer Transformer):
    • Also developed by Google AI, T5 treats all NLP tasks as text-to-text problems.
    • It can be fine-tuned for various tasks, such as translation, summarization, classification, and more.
    • T5’s architecture simplifies the process of adapting it to different NLP tasks.
  4. XLNet:
    • Developed by Google Brain and Carnegie Mellon University, XLNet builds on BERT’s pre-training techniques.
    • It uses a permutation-based training approach, allowing it to capture dependencies between all words in a sentence.
    • XLNet can excel in a wide range of NLP tasks and has demonstrated state-of-the-art performance.
  5. RoBERTa (A Robustly Optimized BERT Pretraining Approach):
    • Developed by Facebook AI, RoBERTa is an optimized version of BERT.
    • It uses larger batch sizes and more training data to improve performance.
    • RoBERTa has achieved top results in various NLP benchmarks.
  6. Turing-NLG (Natural Language Generation):
    • Developed by Microsoft, Turing-NLG is designed for natural language generation tasks.
    • It can create human-like text and has been used for chatbots, content generation, and more.
    • Turing-NLG is known for its fluent and coherent text generation.
  7. ELECTRA (Efficiently Learning an Encoder that Classifies Token Replacements Accurately):
    • Developed by Google Research, ELECTRA focuses on model efficiency.
    • It uses a pre-training approach where it replaces some input tokens and aims to predict those replacements accurately.
    • ELECTRA is known for its ability to achieve competitive results with smaller model sizes.
  8. CTRL (Conditional Transformer Language Model):
    • Also developed by Salesforce Research, CTRL is designed to generate text with specific control over style and content.
    • It can be conditioned to generate text in different styles, tones, and languages.
  9. Turing-NLG2:
    • An advanced version of Turing-NLG developed by Microsoft, it builds upon its predecessor’s capabilities in natural language generation.



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