The "Open-Source" LLM AI Model Lie

They've been lying to you. In Artificial Intelligence, the term "open source" has become a powerful marketing buzzword for large language models (LLMs). However, a closer examination reveals a troubling reality: many AI models labeled as "open source" fall significantly short of the transparency and reproducibility that true open source principles demand.

The Transparency Gap in "Open Source" LLMs

When we examine today's "open" LLMs, we find a concerning pattern of selective disclosure and releases. While companies may release model weights and evaluation results, they frequently withhold critical components that would enable full understanding and reproduction of their systems. The research itself is often withheld or incomplete. 

• Training Data Remains Hidden: Most "open" LLMs don't disclose their training datasets, making it impossible to know what information shaped the model's capabilities and biases. Without transparency into the lineage of training data, researchers and businesses find themselves exposed to potential legal, copyright, and fairness risks1. This opacity also makes it difficult to align AI training datasets with intended use cases1.
  
  
• Selective Technical Disclosure: Even when model weights are shared, companies often withhold details about hyperparameters, optimization techniques, data filtering methodologies, and the specific computational resources used during training. The Data Provenance Initiative at MIT was created specifically to tackle this transparency challenge, recognizing that inconsistently documented and poorly understood AI training datasets open the door to numerous risks1.
  
  
• Alignment and Fine-tuning Mysteries: The processes used to align models with human values and fine-tune them for specific behaviors remain largely undocumented. This includes details about reinforcement learning from human feedback (RLHF) or other techniques that shape the model's final behavior.

When "Open" Isn't Really Open: Case Studies

Meta's Llama model series exemplifies the problem of "open-washing," where models are labeled as open source while falling short of true openness3. Despite Meta's claims, Llama's license contains restrictions that prevent it from meeting the Open Source Initiative's definition of open source4. Specifically, the license forbids using the model to train other language models and requires a special license for applications with more than 700 million monthly users45.

Similarly, Google's Gemma license grants the company the right to "restrict (remotely or otherwise) usage" of the model based on Google's interpretation of its prohibited use policy5. These restrictions make certain models unsuitable for true open source development.

Florian Brand, a research assistant at the German Research Center for Artificial Intelligence, states that licenses like Gemma's and Llama's "cannot reasonably be called 'open source'"5. 

Let me say that again.: The AI LLM Licenses Cannot Be Called "Open Source."

The reality *is* that these custom licenses create significant legal and practical hurdles for businesses wanting to integrate these models into their products5.

The Hidden Details of Censorship

Perhaps most concerning is the lack of transparency around content filtering and censorship in LLMs. Many models implement various filtering techniques to control outputs:

• Undisclosed Content Moderation: Most LLMs implement hate, abuse, and profanity (HAP) filtering without fully disclosing what content is removed or how these decisions are made8. These systems can use classification models to detect and remove certain types of speech, but the specific rules and thresholds are rarely transparent8.
  
  
• Varying Levels of Censorship: Different models may implement minimal, moderate, or strict censorship approaches during training, each with different implications for the model's outputs6. Without transparency about these choices, users cannot make informed decisions about which models align with their values and use cases.
  
  
• Bias Through Filtering: While companies often claim their models are unbiased, the very process of content filtering introduces bias by determining what information is acceptable and what isn't11. These decisions reflect particular ethical, political, and cultural perspectives that may not be explicitly acknowledged.

The Reproducibility Requirement

True open source principles demand reproducibility - the ability for others to recreate, understand, and build upon the work. As some experts argue, replicating an AI model precisely requires full disclosure of all training data2. Without this transparency, we're left with opaque systems that cannot be fully understood or verified.

A small subset of the AI community is working to address these challenges. Initiatives like LLM360 are striving for fully transparent open-source LLMs by sharing frequent intermediate model checkpoints, the entire preprocessed tokenized training dataset, all source code, and comprehensive logs and metrics9. This level of transparency enables true collaborative research and understanding of how these models work.

The Implications of AI Opacity

The consequences of AI opacity extend beyond theoretical concerns:

• Bias Amplification: Without transparency into training data, models may perpetuate historical biases at scale. Examples show how discriminatory data baked into AI models can deploy biases broadly across society12. Open source data science could help address this issue by allowing community oversight7.
  
  
• Limited Innovation: Restrictive licenses and hidden technical details limit the ability of smaller organizations and independent researchers to innovate on top of existing models. As one expert notes, "Small companies without legal teams or money for lawyers will stick to models with standard licenses"5.
  
  
• Ethical Accountability: When training data and filtering mechanisms remain hidden, there's no way for the public or ethicists to evaluate whether models were created responsibly or if they contain problematic content1.
  
  
• Compute Inequality: The computational resources used to train models are rarely disclosed, reinforcing a divide between well-resourced organizations who can afford massive compute and others who cannot compete.

Demanding True Transparency

For AI to truly benefit society, we need to move beyond marketing claims and demand genuine transparency. This means:

1. Comprehensive data documentation, including detailed information about training datasets, their sources, and limitations

2. Full disclosure of technical methodologies, including hyperparameters and optimization techniques

3. Standard open source licenses without special commercial restrictions

4. Transparency around content filtering and moderation approaches

5. Documentation of computational resources required for training
   
   

As AI becomes increasingly embedded in our digital infrastructure, the gap between the promise of "open source" and the reality of closed, proprietary systems threatens to undermine public trust and limit the potential benefits of these technologies19.

The next time you look into an "open-source" AI model, ask the critical questions: Can you see the training data? Do you know how it was filtered? Can you reproduce the training process? Are there hidden restrictions in the license? Only when we demand answers to these questions will we move closer to truly open AI.

#Accelerate