Large language models (LLMs) are advanced AI models that utilize deep learning and vast data sets to generate text, translate languages, and create various types of content. These models can be categorized as either proprietary or open source. In this video, Martin Keen provides a brief overview of LLMs, their relation to foundation models, their functioning, and their potential applications in solving business problems.
Proprietary LLMs are owned by specific companies and can only be accessed by customers who have purchased a license. The usage of these LLMs may be restricted by the terms of the license. On the other hand, open source LLMs are freely available to the public, allowing anyone to access, use, modify, and distribute them. “Open source” refers to the fact that the code and architecture of the LLMs are openly accessible, enabling developers and researchers to utilize, improve, and modify the models as needed.
The benefits of open source LLMs include transparency and flexibility. Enterprises that lack in-house machine learning expertise can employ open source LLMs within their own infrastructure, whether in the cloud or on-premises. This grants them full control over their data, ensuring the preservation of sensitive information within their network and reducing the risk of unauthorized access or data leaks. Open source LLMs also provide transparency regarding their functioning, architecture, training data, methodologies, and usage. Enterprises can inspect the code and gain visibility into the algorithms, fostering trust, facilitating audits, and ensuring ethical and legal compliance. In addition, optimizing open source LLMs leads to reduced latency and improved performance.
Open source LLMs are generally more cost-effective in the long term compared to proprietary LLMs since they do not involve licensing fees. However, operating an LLM still incurs infrastructure costs, whether in the cloud or on-premises, and may require a significant initial investment. Open source LLMs offer added features and allow for community contributions. Enterprises can customize and enhance the LLMs to suit their specific needs, as well as train them on specific data sets. Proprietary LLMs, on the other hand, require collaboration with vendors, consuming valuable time and resources. By utilizing open source LLMs, enterprises can leverage the contributions of the community, engage multiple service providers, and potentially involve internal teams for updates, development, maintenance, and support. This fosters technological innovation and empowers businesses to stay at the forefront of technology while maintaining control over their technology usage and decisions.
Open source LLM models enable a wide range of projects that benefit organizations and their employees. These projects can be developed for internal use or offered as commercial products. Some examples include text generation for writing emails, blog posts, or creative stories, code generation to assist developers in building applications and identifying errors or security flaws, virtual tutoring for personalized learning experiences, content summarization for extracting key information from lengthy articles or research reports, AI-driven chatbots for natural language conversation and answering questions, language translation for accurate and fluent translations across multiple languages, sentiment analysis for analyzing emotional tone in text, and content filtering/moderation to identify and filter out inappropriate or harmful online content.
Various types of organizations, such as IBM, NASA, publishers, journalists, healthcare institutions, and financial industry players, utilize open source LLMs for their specific needs. Open source LLMs offer a wealth of possibilities and are continuously being tracked, ranked, and evaluated through platforms like the Open LLM Leaderboard. Some notable open source LLMs include LLaMa 2, Falcon-40B, Bloom, Vicuna, Alpaca, MPT-7B, MPT-30B, FLAN-T5, StarCoder, RedPajama-INCITE, Cerebras-GPT, and StableLM.
Despite the advantages, it’s important to acknowledge the risks associated with LLMs. These risks include potential “hallucinations” or false information generated by the model, bias in the data used for training, issues regarding consent and data governance, and security vulnerabilities. The understanding of these risks and the implementation of proper education and governance processes are essential for mitigating these concerns and ensuring responsible use of data and AI technologies.