Is Open Source the Future of AI? A Data-Driven Approach

TL;DR

This paper uses data-driven analysis to evaluate the role of open-source development in advancing large language models, highlighting its benefits and challenges for future AI model dissemination.

Contribution

It provides empirical data on open-source contributions to LLMs, informing debates on open versus proprietary AI development strategies.

Findings

Open-source contributions improve model performance.

Open models tend to be smaller with acceptable accuracy loss.

Community engagement positively influences open-source LLM development.

Abstract

Large Language Models (LLMs) have become central in academia and industry, raising concerns about privacy, transparency, and misuse. A key issue is the trustworthiness of proprietary models, with open-sourcing often proposed as a solution. However, open-sourcing presents challenges, including potential misuse, financial disincentives, and intellectual property concerns. Proprietary models, backed by private sector resources, are better positioned for return on investment. There are also other approaches that lie somewhere on the spectrum between completely open-source and proprietary. These can largely be categorised into open-source usage limitations protected by licensing, partially open-source (open weights) models, hybrid approaches where obsolete model versions are open-sourced, while competitive versions with market value remain proprietary. Currently, discussions on where on the spectrum future models should fall on remains unbacked and mostly opinionated where industry leaders are weighing in on the discussion. In this paper, we present a data-driven approach by compiling data on open-source development of LLMs, and their contributions in terms of improvements, modifications, and methods. Our goal is to avoid supporting either extreme but rather present data that will support future discussions both by industry experts as well as policy makers. Our findings indicate that open-source contributions can enhance model performance, with trends such as reduced model size and manageable accuracy loss. We also identify positive community engagement patterns and architectures that benefit most from open contributions.