ChatGPT、DeepSeek及其他最先进大型语言模型的透明度与可访问性综合分析
Comprehensive Analysis of Transparency and Accessibility of ChatGPT, DeepSeek, And other SoTA Large Language Models
AI总结：
本研究系统分析了ChatGPT、DeepSeek等百余种最先进大语言模型的透明度与可访问性，区分开源与开放权重模型，揭示多数模型存在“开源洗白”现象，呼吁负责任的AI实践。
摘要：
尽管关于开源人工智能（AI）的讨论日益增多，现有研究缺乏对最先进（SoTA）大型语言模型（LLMs）透明度与可访问性的探讨。开放源代码促进会（OSI）最近发布了其首个正式的开源软件定义。该定义与标准词典定义及稀疏的已发表文献相结合，为支持包括LLMs在内的AI模型更广泛的可访问性提供了初步框架，但还需更多工作来捕捉AI开放性的独特动态。此外，关于“开源洗白”（open-washing）的担忧已被提出，即模型声称开放但缺乏完全透明度，这限制了这些模型的可复现性、偏见缓解和领域适应性。在此背景下，我们的研究批判性地分析了近五年来的最先进LLMs，包括ChatGPT、DeepSeek、LLaMA等，以评估它们对透明度标准的遵守情况以及部分开放的影响。具体而言，我们从两个视角审视透明度与可访问性：开源模型与开放权重模型。我们的研究结果表明，虽然一些模型被标记为开源，但这并不一定意味着它们是完全开源的。即使在最佳情况下，开源模型也常常不报告模型训练数据、代码以及关键指标，如权重可访问性和碳排放。据我们所知，这是第一项通过开源与开放权重模型的双重视角，系统性地审视超过100种不同最先进LLMs的透明度与可访问性的研究。这些发现为进一步研究开辟了途径，并呼吁采取负责任和可持续的AI实践，以确保这些模型具有更高的透明度、问责性和合乎道德的部署。
英文摘要：
Despite increasing discussions on open-source Artificial Intelligence (AI), existing research lacks a discussion on the transparency and accessibility of state-of-the-art (SoTA) Large Language Models (LLMs). The Open Source Initiative (OSI) has recently released its first formal definition of open-source software. This definition, when combined with standard dictionary definitions and the sparse published literature, provide an initial framework to support broader accessibility to AI models such as LLMs, but more work is essential to capture the unique dynamics of openness in AI. In addition, concerns about open-washing, where models claim openness but lack full transparency, has been raised, which limits the reproducibility, bias mitigation, and domain adaptation of these models. In this context, our study critically analyzes SoTA LLMs from the last five years, including ChatGPT, DeepSeek, LLaMA, and others, to assess their adherence to transparency standards and the implications of partial openness. Specifically, we examine transparency and accessibility from two perspectives: open-source vs. open-weight models. Our findings reveal that while some models are labeled as open-source, this does not necessarily mean they are fully open-sourced. Even in the best cases, open-source models often do not report model training data, and code as well as key metrics, such as weight accessibility, and carbon emissions. To the best of our knowledge, this is the first study that systematically examines the transparency and accessibility of over 100 different SoTA LLMs through the dual lens of open-source and open-weight models. The findings open avenues for further research and call for responsible and sustainable AI practices to ensure greater transparency, accountability, and ethical deployment of these models.(DeepSeek transparency, ChatGPT accessibility, open source, DeepSeek open source)
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
