How Open Are Open-Source LLM Models, Really?

, which they call an open-source, state-of-the-art large language model, has been sparking discussion. While proprietary models and corporations are under intense scrutiny for having control over the models—hence the direct control on public domain conversations and interface—open-source AI has also been controversial in certain quarters of academia, as well as among regulators. For example, anpublished in IEEE Spectrum called open-source AI “uniquely dangerous.

The purist, truly open-source distinction advocates for raising the bar on openness in AI development by providing complete access to its development tools, such as pretraining data, training codes, model weights and evaluation tools. Furthermore, this should include granting more democratized access to computing, funding, the ability to scrutinize and modify models and the opportunity to participate in or opt out of the data collection process.

This level of secrecy aims to protect intellectual property and commercial interests but hinders collaborative development and transparency in how AI algorithms make decisions that can significantly affect the public domain.Slightly up the scale are source-available models. An example of this approach could be seen where certain components’ source code can be used under specific conditions—for example, Meta’s LLama 2 model.

The call for a thorough action on both ends of the spectrum, especially for those who claim to be truly open but instead use the open-source technology for furthering a particular agenda, is a conversation that must be had. It’s worrisome how easily these AI systems can be hijacked for malicious purposes.

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