What Are Large Language Models (LLMs)?

Part 1 of a 13-part series about LLMs

Large Language Models (LLMs) are AI systems built using deep learning, primarily leveraging the Transformer architecture. Trained on vast text datasets, they learn patterns, context, and relationships in language, enabling them to perform tasks like text generation, translation, summarization, and question-answering. Their ability to understand and generate coherent, contextually relevant text comes from the scale and depth of their training.

A Brief History of LLM Development

LLMs didn’t appear overnight; their development reflects decades of research and innovation in the field of Natural Language Processing (NLP).

The Early Days of NLP

• 1950s-1980s: Early NLP systems used symbolic AI, rule-based methods, and handcrafted grammars. While foundational, these systems struggled with complexity and nuance in language.
• 1990s-2000s: The rise of statistical models (like Hidden Markov Models and n-grams) brought more sophistication, but context handling was still limited.

The Shift to Deep Learning

• 2013: The introduction of Word2Vec by Google marked a turning point. It allowed for efficient word embeddings, enabling models to understand semantic similarities.
• 2014: The Sequence-to-Sequence (Seq2Seq) model and Long Short-Term Memory (LSTM) networks began improving context understanding.

The Transformer Revolution

• 2017: Google researchers published the seminal paper, “Attention Is All You Need,” introducing the Transformer architecture. This breakthrough allowed models to process sequences in parallel, improving efficiency and performance dramatically.
• 2018 and Beyond: Transformer-based models like BERT (Bidirectional Encoder Representations from Transformers), GPT (Generative Pre-trained Transformer), and T5 took NLP by storm, pushing boundaries in language comprehension and generation.

Source: Xiong et al. ChatGPT’s One-year Anniversary: Are Open-Source Large Language Models Catching up?

Key Milestones in LLM Evolution

• 2018: OpenAI releases GPT-1, showcasing generative capabilities with modest training data.
• 2019: Google’s BERT achieves state-of-the-art results in NLP benchmarks.
• 2020: GPT-3 launches with 175 billion parameters, setting a new standard for scale and performance.
• 2023: OpenAI releases GPT-4, integrating multimodal capabilities (text + image input).

Types of Large Language Models (LLMs)

1. Autoregressive Models (e.g., GPT Series)

• These models generate text one word at a time, predicting the next word based on the previous ones. They are typically used for text-generation tasks.
• Example: GPT-3, GPT-4.

2. Masked Language Models (e.g., BERT)

• These models are trained to predict missing words in a sentence by focusing on both the left and right context. They are primarily used for tasks like text classification and question answering.
• Example: BERT, RoBERTa, DistilBERT.

3. Sequence-to-Sequence Models (e.g., T5, BART)

• These models are designed to transform an input sequence (e.g., a sentence) into a different output sequence, such as translation or summarization tasks.
• Example: T5, BART.

4. Multimodal Models

• These models handle both text and other data types like images or audio, allowing for cross-modal understanding. They are useful in tasks that involve both language and visual content.
• Example: CLIP (combines text and image), DALL·E (text-to-image generation).

5. Encoder-Decoder Models (e.g., T5, BART)

• A combination of both encoder and decoder architectures, these models are useful for generating contextually rich text from input data. The encoder processes the input, and the decoder generates the output.
• Example: T5, BART.

6. Multilingual Models

• These models are trained to understand and generate text in multiple languages, making them ideal for tasks like translation and cross-lingual information retrieval.
• Example: mBERT, XLM-R.

These types of LLMs cater to different needs in language processing, with each offering strengths for specific tasks.

Open-Source vs. Closed-Source LLMs

Open-Source LLMs

Open-source LLMs are freely available for anyone to use, modify, and distribute. They often foster collaboration and community-driven improvements. These models typically have pre-trained versions that can be fine-tuned for specific tasks.

Examples:

1. GPT-2 (OpenAI)

• OpenAI released GPT-2 as open-source, allowing developers to use and fine-tune it for a variety of NLP tasks.

2. BERT (Google)

• BERT is an open-source pre-trained model, widely used for tasks like text classification, named entity recognition, and more.

3. T5 (Google)

• T5 is a flexible transformer model for a wide range of text tasks, released as open-source by Google.

4. EleutherAI (GPT-Neo, GPT-J)

• EleutherAI developed open-source versions of GPT models like GPT-Neo and GPT-J, aimed at replicating and expanding upon the capabilities of GPT-3.

5. Fairseq (Facebook AI)

• Fairseq is a sequence-to-sequence learning toolkit by Facebook AI Research, with implementations of various LLMs like BART and RoBERTa.

Advantages of Open-Source LLMs:

• Transparency: Anyone can examine the code and models to understand how they work.
• Customization: Models can be adapted and fine-tuned to suit specific needs.
• Community Support: Open-source projects benefit from active contributions and peer-reviewed updates.

Closed-Source LLMs

Closed-source LLMs are proprietary models, with access controlled by the companies that developed them. These models are usually available through API services, meaning users can interact with the model but don’t have access to the underlying code or weights.

Examples:

1. GPT-3 (OpenAI)

• GPT-3, while developed by OpenAI, is closed-source and only accessible through OpenAI’s API.

2. PaLM (Google)

• PaLM is a large language model developed by Google, but its full model is not available to the public. It’s accessible via Google’s API for specific applications.

3. Claude (Anthropic)

• Claude is a closed-source model developed by Anthropic, designed to be safer and more interpretable.

4. Cohere

• Cohere provides large language models via API but doesn’t make the underlying models open-source.

5. BLOOM (BigScience)

• While BLOOM itself is open-source, it is important to note that some versions of similar large models, like those from proprietary companies, are not open-source and are only available as part of a paid API service.

Advantages of Closed-Source LLMs:

• Better Fine-Tuning and Optimization: Proprietary companies have the resources to fine-tune models for specific use cases, offering highly optimized performance.
• Scalability: Many closed-source LLMs are hosted and managed in the cloud, providing users with easy access and scaling without managing the infrastructure.
• Support and Maintenance: Closed-source LLMs often come with dedicated support and constant updates from the companies behind them.

Source: https://datasciencedojo.com/blog/open-source-llm/

This post serves as a foundation for our 13-part series. In the coming days, we’ll delve deeper into the math, mechanics, and applications that make LLMs the cornerstone of modern AI.

Stay tuned for Part 2: The Mathematics Behind LLMs, where we’ll explore the math powering these models and uncover the magic behind their capabilities.

Let’s embark on this journey together! 🚀