We discussed large language models (LLMs) in a previous blog – https://www.vamsitalkstech.com/ai/mckinsey-on-the-impact-of-large-language-models-on-industry-verticals/. LLMs are a type of foundation model that are trained on massive datasets of text and code. They can be used for a variety of tasks, such as generating text, translating languages, and writing different kinds of creative content. Andreesen Horowitz recently discussed the emerging architectures for large language model (LLM) applications.
Gen AI and LLMs
In recent years, the fields of artificial intelligence (AI) and machine learning (ML) have witnessed significant advancements, particularly in the realm of generative models. Generative AI, often referred to as Gen AI, and Large Language Models (LLMs) are two prominent outcomes of these advancements that have far-reaching implications for various industries and technological landscapes.
Gen AI encompasses a class of machine learning techniques that aim to create data, such as images, text, audio, and more, that is similar to data seen in the training process. These models have the ability to generate new content by learning the underlying patterns and structures present in the training data. One of the most notable achievements in the realm of Gen AI is the development of Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs). GANs consist of two neural networks, the generator and the discriminator, which work together in a competitive manner. The generator creates data instances, and the discriminator evaluates whether the generated data is real or artificial. Through iterations, GANs progressively improve the quality of generated content, making them invaluable in generating realistic images, videos, and even audio.
VAEs, on the other hand, are used for learning latent representations of data. They enable the generation of new instances by sampling from learned latent spaces. VAEs have applications in tasks like image generation, data compression, and more.
Large Language Models (LLMs): Large Language Models (LLMs) are a specific category of generative AI focused on generating human-like text. These models, often built using transformer architectures, have the capacity to understand and produce coherent and contextually relevant text based on the input they receive. OpenAI’s GPT (Generative Pre-trained Transformer) series is a prime example of LLMs.
GPT-3, for instance, has billions of parameters and can perform a range of language-related tasks, such as language translation, text generation, content summarization, and even code generation. By training on massive datasets, GPT-3 has learned to mimic human language patterns, making it a versatile tool for various applications.
The Architecture of LLMs:
a16z proposes a new architecture (https://a16z.com/2023/06/20/emerging-architectures-for-llm-applications/) for LLM applications that is based on a number of key principles:
- Decoupling: The different components of the architecture are decoupled, which allows them to be scaled independently.
- Modularity: The architecture is modular, which makes it easy to add new features and capabilities.
- Flexibility: The architecture is flexible, which allows it to be adapted to different use cases.
A number of tools and technologies can be used to implement the new architecture. These include:
- Model providers: Model providers offer pre-trained LLMs that can be used to build applications.
- Public clouds: Public clouds offer computing resources that can be used to train and deploy LLM applications.
- Orchestration tools: Orchestration tools can be used to manage the different components of an LLM application.
A handy list of projects are provided for easy reference:
a16z concludes by discussing the future of LLM applications. The author argues that LLMs have the potential to revolutionize the way that software is developed and used. However, he also cautions that there are a number of challenges that need to be addressed before LLMs can be widely adopted.
Other key highlights from the article:
- LLMs are typically trained on datasets that contain billions or even trillions of words.
- The training process for an LLM can take weeks or even months.
- LLMs can be deployed on a variety of computing platforms, including public clouds, on-premises servers, and edge devices.
- LLMs can be used to build a wide range of applications, including chatbots, language translation tools, and creative content generators.
Conclusion
The traditional architecture for LLM applications is no longer sufficient. This is because LLMs are very large and complex models, and they require a lot of computing resources to train and deploy. Extending the traditional LLM architecture concepts, coupled with large-scale pretraining on diverse textual data, can revolutionize natural language processing and make LLMs incredibly versatile. Thus, making them capable of performing tasks ranging from text generation and translation to question answering and summarization in industry verticals, among many other applications.
Feaured Image By svstudioart