What are Generative AI models?

Understanding Large Language Models (LLMs) and Foundation Models

Over the past few months, large language models (LLMs) like chatGPT have gained immense popularity and are revolutionizing the field of AI. These models have the potential to drive enterprise value and are being used for various tasks, from writing poetry to planning vacations. In this article, we will explore the concept of LLMs and their role as foundation models in driving business value.

What are Foundation Models?

Foundation models are a class of models that have emerged as a new paradigm in the field of AI. Previously, AI applications were built using task-specific models trained on specific data. However, with the advent of foundation models, a single model can be used for multiple applications and tasks. These models are trained on vast amounts of unstructured data in an unsupervised manner, giving them the ability to transfer to different tasks.

Generative AI and Tuning

Foundation models, being generative AI models, have the capability to generate new content based on previously seen data. For example, a model trained on language data can predict the next word in a sentence based on the words that came before it. However, these models can also be tuned to perform traditional natural language processing (NLP) tasks by introducing a small amount of labeled data. This process, known as tuning, allows the model to update its parameters and perform specific NLP tasks like classification or named-entity recognition.

Prompting and Low-Labeled Data Domains

Even without a large amount of labeled data, foundation models can still be effective in low-labeled data domains. Through a process called prompting or prompt engineering, these models can be applied to tasks like classification. By providing a sentence and asking a question related to sentiment analysis, the model can generate the next word, which serves as the answer to the classification problem.

Advantages and Disadvantages of Foundation Models

Foundation models offer several advantages, including high performance and productivity gains. These models have been trained on massive amounts of data, allowing them to outperform models trained on limited data points. Additionally, they require less labeled data for task-specific models due to their pre-training on unlabeled data.

However, there are also disadvantages to consider. The compute cost of training and running inference on these models can be high, making them less accessible for smaller enterprises. Trustworthiness is another concern, as these models are trained on scraped language data from the internet, which may contain biases or toxic information. The lack of transparency regarding the exact datasets used for training can also raise trustworthiness issues.

IBM’s Innovations and Applications

IBM recognizes the potential of foundation models and is actively working on improving their efficiency, trustworthiness, and reliability. The company is innovating across various domains, including language, vision, code, chemistry, and climate change. IBM Research is developing models for products like Watson Assistant, Watson Discovery, Maximo Visual Inspection, and Ansible code models under Project Wisdom. These models aim to enhance business operations and provide valuable solutions in different industries.

Frequently Asked Questions (FAQs)

1. What are large language models (LLMs)?

Large language models (LLMs) are AI models that have the ability to generate human-like text based on the patterns and information they have learned from vast amounts of training data. They can be used for various tasks, including writing, translation, and conversation.

2. How do foundation models differ from traditional AI models?

Foundation models are a new paradigm in AI where a single model can be used for multiple applications and tasks. Unlike traditional AI models that are trained on task-specific data, foundation models are trained on large amounts of unstructured data, allowing them to transfer their knowledge to different tasks.

3. Can foundation models be used in low-labeled data domains?

Yes, foundation models can be effective in low-labeled data domains. Through a process called prompting or prompt engineering, these models can be applied to tasks even with limited labeled data. By providing a sentence and asking a question related to the task, the model can generate the next word, which serves as the answer.

4. What are the advantages of foundation models?

Foundation models offer high performance due to their extensive training on large amounts of data. They also provide productivity gains as they require less labeled data for task-specific models. These models can outperform models trained on limited data points and enhance efficiency in various applications.

5. What are the disadvantages of foundation models?

Foundation models have high compute costs, making them expensive to train and run inference. They also raise trustworthiness concerns as they are trained on scraped language data from the internet, which may contain biases or toxic information. The lack of transparency regarding the training datasets further adds to the trustworthiness issues.

In conclusion, large language models and foundation models are transforming the field of AI and driving enterprise value. These models offer high performance, productivity gains, and the ability to transfer knowledge to different tasks. However, they also come with compute cost and trustworthiness challenges. IBM is actively working on improving these models and applying them across various domains to provide innovative solutions in business settings.