Skip to content

What are Large Language Models?

Are you curious about Large Language Models (LLMs) and why they’re making headlines? Even if the term "LLM" is unfamiliar, chances are you’ve heard of ChatGPT and its incredible capabilities. LLMs are revolutionizing how we interact with technology, work, and live. Let’s explore what they are and why they matter.

Natural Language Processing

Natural Language Processing (NLP) is a subset of Artificial Intelligence that focuses on teaching machines to understand, interpret, and generate human language. Whether it's translating text, summarizing documents, or analyzing sentiment in tweets, NLP uses machine learning and deep learning techniques to solve language-related problems.

For example, let’s consider Sentiment Analysis, a common NLP task. Here, the goal is to determine the sentiment of a piece of text, such as whether a tweet expresses happiness, anger, or sarcasm. In the past, building models for such tasks involved training from scratch, which came with challenges:

  • Limited language understanding: Models trained from scratch often lacked nuanced comprehension.
  • Massive data requirements: Achieving high accuracy demanded enormous datasets.
  • Limited reusability: Models trained for one task couldn’t easily be adapted for others.

How Do Humans Learn Language?

Humans don’t learn language by memorizing rules for every possible scenario. Instead, we:

  1. Learn through exposure: From a young age, we’re exposed to vast amounts of spoken and written language.
  2. Adapt to new tasks: Once we understand a language, we can apply our skills to different tasks without extensive retraining.
  3. Require minimal examples: For most tasks, we don’t need thousands of examples to perform well.

This human-like adaptability inspired AI researchers to create systems capable of understanding and performing various NLP tasks without needing to start from scratch each time.

Language Models

AI researchers developed Language Models to mimic human language understanding and adaptability. These models are trained on lots of text data, giving them a deep grasp of linguistic patterns and structures. Here are their key characteristics:

  • Extensive training: LMs are trained on vast datasets, encompassing everything from classic literature to social media posts.
  • Task flexibility: They can be finetuned for specific tasks with minimal additional data.
  • Transformers at their core: The introduction of Transformer architecture revolutionized LMs by enabling better performance on NLP tasks. This breakthrough led to a surge in NLP advancements and applications.

Even though these models were better than ever, they still had limitations. Their performance is not perfect, and they still made mistakes.

Large Language Models

Much like how animals with larger brains and more intricate neuron connections often exhibit higher intelligence, Large Language Models (LLMs) scale up neural networks to achieve remarkable capabilities. These LLMs:

  • Massive size: Contain billions of parameters (learnable weights).
  • Massive data: Trained on massive text data (trillions of tokens).
  • Advanced performance: Excel at understanding context, generating coherent text, and adapting to diverse tasks.
  • Huge resource requirements: Require lots of compute power for training and inference.

Challenges in Training LLMs

While LLMs are groundbreaking, building and deploying them is no small feat:

  • High computational costs: Training an LLM requires advanced infrastructure and significant financial investment.
  • Large datasets: Ensuring diverse and representative training data is critical.
  • Accessibility: Some LLMs are kept proprietary by the companies that developed them, limiting public access.

Despite these challenges, the results are impressive. Companies like OpenAI, Anthropic, and Meta have developed powerful LLMs such as GPT, Claude and LLama Series LLMs that are pushing the boundaries of what’s possible with LLMs.

Open Source vs. Closed Source LLMs

Open Source LLMs

Open source LLMs make their architectures, pre-trained weights publicly available, allowing developers and researchers to:

  • Customize LLMs for specific needs.
  • Contribute improvements to the AI community.
  • Use these LLMs without proprietary restrictions.

Examples include: Llama by Meta, Gemma by Google

Closed Source LLMs

Closed source LLMs are proprietary and often accessible only via APIs. While they’re highly optimized, their usage may be limited by licensing fees and restrictions. Examples include:

Examples include: GPT by OpenAI, Claude by Anthropic, Gemini by Google

The Future of LLMs

LLMs are transforming industries by enabling:

  • Personalized AI assistants: Tools like ChatGPT and voice assistants that adapt to your preferences.
  • Content creation: Generating high-quality text for blogs, articles, and even creative writing.
  • Automated coding: Writing code and debugging.
  • Agentic AI: Creating AI agents that can perform complex tasks.

As LLMs continue to evolve, they will unlock new possibilities, making AI an even more integral part of our lives.