Proprietary vs. Open Source Large Language Models (LLMs)

Introduction

Large Language Models (LLMs) have become a prominent aspect of artificial intelligence, particularly in the field of natural language processing. These models, such as GPT-3, have the ability to generate human-like text and have a wide range of applications, from chatbots to content generation. However, the LLM landscape can be divided into two distinct categories: proprietary and open source models. In this article, we will explore the key differences, advantages, and disadvantages of proprietary and open source LLMs.

Proprietary LLMs

Proprietary LLMs are developed and owned by specific companies. These models are often associated with large parameter sizes and may include licenses that restrict their usage. While proprietary LLMs can offer powerful language generation capabilities, they come with limitations. Some leading proprietary LLMs have parameter counts in the billions, but the exact numbers are often undisclosed due to proprietary reasons. It is important to note that bigger parameter sizes do not necessarily equate to better performance.

Open Source LLMs

Open source LLMs, in contrast, are freely available for anyone to access, use, and modify. Developers and researchers can fine-tune these models to suit specific use cases and can even train them on custom datasets. The open source model ecosystem is challenging the proprietary LLM business model, offering several key benefits:

Transparency: Open source LLMs often provide greater transparency regarding their architecture and training data, fostering trust and understanding among users.

Fine-Tuning: Users can fine-tune open source LLMs to adapt them to specific tasks, making them more versatile and applicable across various domains.

Community Contributions: Open source LLMs benefit from contributions from a diverse community of developers and researchers, which ensures continuous improvement and innovation.

Wide Adoption: Numerous organizations, including NASA, IBM, and healthcare institutions, have embraced open source LLMs for applications ranging from geospatial data analysis to healthcare diagnostics.

Notable Open Source LLMs

Several open source LLMs have gained prominence in the field:

Llama 2: Provided by Meta AI, Llama 2 encompasses pre-trained and fine-tuned generative text models, ranging from 7 billion to 70 billion parameters. It is licensed for commercial use.

Vicuna: Derived from the Llama model, Vicuna has been fine-tuned for specific tasks, demonstrating the adaptability of open source LLMs.

Bloom by BigScience: This multilingual language model, created collaboratively by over 1000 AI researchers, showcases the potential of open source cooperation in developing LLMs.

Risks Associated with LLMs

Both proprietary and open source LLMs share certain risks, including:

Incorrect Output: LLMs can generate fluent-sounding text that is factually incorrect or misleading, leading to inaccuracies.

Bias: Bias can emerge from the data used to train LLMs, resulting in content that reflects existing biases or stereotypes.

Security Concerns: LLMs may unintentionally leak Personally Identifiable Information (PII) and can be exploited by cybercriminals for malicious activities such as phishing.