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

• URL: http://arxiv.org/abs/2501.16403v1
• Date: Mon, 27 Jan 2025 09:03:49 GMT
• Title: Is Open Source the Future of AI? A Data-Driven Approach
• Authors: Domen Vake, Bogdan Šinik, Jernej Vičič, Aleksandar Tošić,
• Abstract summary: Large Language Models (LLMs) have become central in academia and industry. Key issue is the trustworthiness of proprietary models, with open-sourcing often proposed as a solution. Open-sourcing presents challenges, including potential misuse, financial disincentives, and intellectual property concerns.
• Score: 41.94295877935867
• License:
• 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.

Related papers

• The Open Source Advantage in Large Language Models (LLMs) [0.0]
  Large language models (LLMs) have rapidly advanced natural language processing, driving significant breakthroughs in tasks such as text generation, machine translation, and domain-specific reasoning. The field now faces a critical dilemma in its approach: closed-source models like GPT-4 deliver state-of-the-art performance but restrict accessibility, and external oversight. Open-source frameworks like LLaMA and Mixtral democratize access, foster collaboration, and support diverse applications, achieving competitive results through techniques like instruction tuning and LoRA.
  arXiv  Detail & Related papers  (2024-12-16T17:32:11Z)
• Rethinking Scale: The Efficacy of Fine-Tuned Open-Source LLMs in Large-Scale Reproducible Social Science Research [0.0]
  Large Language Models (LLMs) are distinguished by their architecture, which dictates their parameter size and performance capabilities. Social scientists have increasingly adopted LLMs for text classification tasks, which are difficult to scale with human coders. This study demonstrates that small, fine-tuned open-source LLMs can achieve equal or superior performance to models such as ChatGPT-4.
  arXiv  Detail & Related papers  (2024-10-31T20:26:30Z)
• On the modification and revocation of open source licences [0.14843690728081999]
  This paper argues for the creation of a subset of rights that allows open source contributors to force users to update to the most recent version of a model. Legal, reputational and moral risks related to open-sourcing AI models could justify contributors having more control over downstream uses.
  arXiv  Detail & Related papers  (2024-05-29T00:00:25Z)
• Open-Source AI-based SE Tools: Opportunities and Challenges of Collaborative Software Learning [23.395624804517034]
  Large Language Models (LLMs) have become instrumental in advancing software engineering (SE) tasks. The collaboration of these AI-based SE models hinges on maximising the sources of high-quality data. Data especially of high quality, often holds commercial or sensitive value, making it less accessible for open-source AI-based SE projects.
  arXiv  Detail & Related papers  (2024-04-09T10:47:02Z)
• On the Societal Impact of Open Foundation Models [93.67389739906561]
  We focus on open foundation models, defined here as those with broadly available model weights. We identify five distinctive properties of open foundation models that lead to both their benefits and risks.
  arXiv  Detail & Related papers  (2024-02-27T16:49:53Z)
• OLMo: Accelerating the Science of Language Models [165.16277690540363]
  Language models (LMs) have become ubiquitous in both NLP research and in commercial product offerings. As their commercial importance has surged, the most powerful models have become closed off, gated behind proprietary interfaces. We believe it is essential for the research community to have access to powerful, truly open LMs. We have built OLMo, a competitive, truly Open Language Model, to enable the scientific study of language models.
  arXiv  Detail & Related papers  (2024-02-01T18:28:55Z)
• On the Safety of Open-Sourced Large Language Models: Does Alignment Really Prevent Them From Being Misused? [49.99955642001019]
  We show that open-sourced, aligned large language models could be easily misguided to generate undesired content. Our key idea is to directly manipulate the generation process of open-sourced LLMs to misguide it to generate undesired content.
  arXiv  Detail & Related papers  (2023-10-02T19:22:01Z)
• Open-Sourcing Highly Capable Foundation Models: An evaluation of risks, benefits, and alternative methods for pursuing open-source objectives [6.575445633821399]
  Recent decisions by leading AI labs to either open-source their models or to restrict access to their models has sparked debate. This paper offers an examination of the risks and benefits of open-sourcing highly capable foundation models.
  arXiv  Detail & Related papers  (2023-09-29T17:03:45Z)
• Foundation Models and Fair Use [96.04664748698103]
  In the U.S. and other countries, copyrighted content may be used to build foundation models without incurring liability due to the fair use doctrine. In this work, we survey the potential risks of developing and deploying foundation models based on copyrighted content. We discuss technical mitigations that can help foundation models stay in line with fair use.
  arXiv  Detail & Related papers  (2023-03-28T03:58:40Z)
• Towards Inheritable Models for Open-Set Domain Adaptation [56.930641754944915]
  We introduce a practical Domain Adaptation paradigm where a source-trained model is used to facilitate adaptation in the absence of the source dataset in future. We present an objective way to quantify inheritability to enable the selection of the most suitable source model for a given target domain, even in the absence of the source data.
  arXiv  Detail & Related papers  (2020-04-09T07:16:30Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.