An Introduction to Artificial Intelligence Language Models

June 23, 2023

An Introduction to Artificial Intelligence Language Models

Research is done by Dan

What was once confined to the pages of sci-fi novels and the cinema screens has become an everyday reality. With each passing day, AI continues to evolve and shape our lives in unprecedented ways—this is all thanks to the creation of language models!

Just in 2020, Silicon Valley was buzzing about a remarkable AI known as GPT-3. Developed by the renowned organization OpenAI, headquartered in San Francisco, GPT-3 quickly became the talk of the town.

How did it achieve this feat? By devouring a vast corpus of billions of words extracted from books, articles, and websites, GPT-3 acquired an unparalleled understanding of human language.

Representing the pinnacle of AI capabilities, this extraordinary “large language model” had the astonishing ability to generate coherent and fluent text.

What Is a Language Model?

A language model is a machine learning model specifically designed to represent and understand the intricacies of human language. It serves as a foundation for various natural language processing (NLP) tasks.

At its core, a language model is trained to estimate the probability distribution of words. In simpler terms, it strives to predict the most appropriate word to fill in a missing part of a sentence or phrase based on the surrounding context of the given text.

For example:

Sentence: The cat is ____

This model will deduce that the next word to fill in the blank is “sleeping.”

So, a language model is all about capturing how we use words in writing, not just sticking to grammar rules. This way, the model can generate language that is more like how humans talk and understand.

The next question would be: what puts GPT at the top among other advanced language models?

What is GPT?

GPT, short for “Generative Pre-trained Transformer,” is a groundbreaking language model. It has revolutionized the field of natural language processing with its ability to generate coherent and contextually relevant text.

Through a combination of pre-training and fine-tuning, GPT learns from vast amounts of data and acquires a broad knowledge base. This enables it to perform a wide range of natural language tasks. GPT’s remarkable ability to generate human-like text has sparked both awe and concern, leading to ongoing efforts to ensure responsible and ethical use.

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9 Natural Language Processing that GPT Can Do

As a language modeling created by OpenAI, GPT is capable of performing various natural language processing tasks. Here are nine tasks that GPT can do:

Language Translation

GPT generates high-quality translations across different languages, facilitating multilingual communication and localization efforts.

Text Completion

GPT can generate text by suggesting coherent and contextually appropriate continuations, providing assistance in writing, content generation, and autocomplete features.

Text Summarization

GPT condenses lengthy documents into concise summaries while retaining key information, which is valuable for news aggregation and document analysis.

Question Answering

GPT understands and responds to questions, enabling accurate and informative answers for virtual assistants and information retrieval systems.

Sentiment Analysis

GPT discerns positive or negative sentiment in text, assisting in understanding and analyzing emotions in user feedback or social media posts.

Dialogue Generation

GPT generates word sequences that are both contextually relevant and realistic, making it an invaluable tool for developing conversational agents or chatbots.

Text Classification

GPT categorizes text into predefined classes or categories, aiding in tasks such as content filtering, spam detection, or topic labeling.

Named Entity Recognition

GPT identifies and extracts named entities (e.g., person names, locations) from text, which is useful for information extraction and data analysis.

Semantic Parsing

GPT is also a language model designed to understand the structure and meaning of natural language queries, supporting the conversion of user queries into machine-readable representations for information retrieval or database queries.

These capabilities demonstrate the versatility and practicality of GPT across various NLP tasks, showcasing its potential in automating and enhancing language-related processes.

Comparison of Language Models

In this blog, we will focus on three language models: OpenAI Embeddings, PrivateGPT, and GPT-Neo.

Each of these has its unique strengths as a language model, and we’ll show you how to implement them in your applications.

OpenAI Embeddings

OpenAI embeddings are pre-trained language models that can represent words, phrases, or documents as numerical vectors, often called embeddings. These embeddings capture the semantic meaning and contextual information of the text.

OpenAI has released various versions of language models, such as GPT-3, that offer powerful contextual embeddings. These embeddings have found applications in natural language understanding, sentiment analysis, text classification, and more.

Implementation Steps:

  1. Scrape website data to create a dataset.
  2. Select a compatible language model like davinci-003 or davinci-002 from OpenAI.
  3. Prepare and send the dataset to the OpenAI API.
  4. Receive the response from the API, which contains the analyzed results.

Example Output:

example output of OpenAI
example output of OpenAI


These language models often work with training data that is not publicly available. PrivateGPT, for example, uses data from the company’s internal documents to fine-tune the model.

So, When you need to prioritize user privacy, then PrivateGPT is the language model to use.

OpenAI recognizes the importance of privacy and aims to provide options that allow users to leverage the benefits of language models while keeping their data confidential. In this way, PrivateGPT aims to strike a balance between the utility of the model and the privacy concerns associated with processing sensitive information.

Implementation Steps:

  1. Scrape website data to create a dataset.
  2. Select a compatible language model like ggml-gpt4all-j-v1.3-groovy or ggml-gpt4all-j-v1.3-jazzy.
  3. Prepare the dataset.
  4. Process the dataset using privateGPT.

Example Output:

example output of PrivateGPT


Another language model is GPT-Neo. This is an open-source project that aims to replicate the success of the GPT series of models using smaller-scale architectures. It is developed by EleutherAI, a community-driven research organization.

GPT-Neo models are designed to be more accessible, computationally efficient, and easier to train compared to their larger counterparts. GPT-Neo models offer capabilities similar to those of GPT models but with reduced computational requirements, making them suitable for a broader range of users and applications.

Note: Implementation is not provided due to the resource-intensive nature of fine-tuning this model with a custom dataset.

Example Output:

Note: Due to implementation constraints, this dataset cannot be created by scraping a website. Example output is based on the model dataset.

example output of GPT-Neo

Limitations and Challenges of Language Models

Despite the remarkable capabilities of neural language models, it is important to acknowledge their limitations.

Although many language models have undergone extensive training on vast amounts of textual data, enabling them to comprehend natural language and produce human-like text, they fall short in tasks that demand reasoning and general intelligence.

Even a good language model can’t perform tasks involving:

  • common-sense knowledge,
  • getting the gist of abstract ideas, and
  • making educated guesses from incomplete information.

If you have used LLMs before, you would see disclaimers that say outputs may not be as accurate or this language mode may make mistakes. This is because GPT models and other LLMs rely solely on statistical language models in the data without truly understanding the underlying concepts.

Moreover, large language models also lack the capability to comprehend the world as humans do. So, alone, they cannot make decisions or take actions in the physical realm.


Comparison table of OpenAI Embeddings, PrivateGPT, and GPT-Neo language models

In conclusion, OpenAI Embeddings, PrivateGPT, and GPT-Neo are three notable language models that contribute to the field of natural language processing in different ways.

Each of these language models contributes to the advancement of natural language processing research and applications, catering to different priorities and needs. OpenAI Embeddings excels in contextual embeddings, PrivateGPT prioritizes user privacy, and GPT-Neo offers accessibility and efficiency.

Researchers and practitioners can choose the most suitable model based on their specific requirements, taking into account factors such as performance, privacy considerations, resource constraints, and the desired level of model customization.

StarTechUP is a software development company in the Philippines that offers custom software development services. If you need a custom language model or are looking for any other software development solutions, don’t hesitate to reach out to us!

We look forward to helping you drive innovation with our technology services!

About the author: Andrea Jacinto - Content Writer

A content writer with a strong SEO background, Andrea has been working with digital marketers from different fields to create optimized articles which are informative, digestible, and fun to read. Now, she's writing for StarTechUP to deliver the latest developments in tech to readers around the world. View on Linkedin