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We’re witnessing an explosion of AI models. However, a growing problem is emerging: the names of these models are becoming increasingly convoluted, a labyrinth of acronyms and technical jargon that leaves even enthusiastic AI users scratching their heads.
We Need Simpler Nomenclature for AI Models
As innovative as each new AI model may be, their convoluted names are a serious barrier for users trying to understand and differentiate between models. This complexity not only hinders accessibility for the average user but also creates a significant barrier to understanding and utilizing the full potential of these powerful tools.
For example, when Chinese tech giantAlibaba launched its Qwen2.5-Coder-32B model, who really understood what it could do? You had to dig through the jargon to find out.
While AI companies often decide on a creative product name, like Gemini, Mistral, or Llama, a model’s eventual name incorporates certain technical attributes, like version or iteration number, architecture or type, parameter count, and other specific characteristics. For example, the nameLlama 2 70B-chattells us that this model by Meta (Llama) is a large language model with 70 billion parameters (70B) and is specifically designed for conversational purposes (-chat).
In essence, an AI model’s name serves as a shorthand for its key attributes, allowing researchers and technical users to quickly understand its nature and purpose—but is mostly gobbledygook to the layperson.
Consider the scenario where a user wants to choose between the latest models for a specific task. They are faced with options like “Gemini 2.0 Flash Thinking Experimental”, “DeepSeek R1 Distill Qwen 14B”, “Phi-3 Medium 14B”, and “GPT-4o.” Without diving deep into technical specifications, distinguishing between these models becomes a daunting task.
A cascade of model names, each more cryptic than the last, underscores the need for a fundamental shift in how we label and present AI models. An AI model name should ideally be a simple, clear, and memorable representation of its purpose and capabilities.
Imagine if cars were named according to their engine specifications and suspension types rather than simple, evocative names like “Mustang” or “Civic.” The current naming conventions for AI models often prioritize technical specifications over user-friendliness. And while some terms are essential for researchers, they are largely meaningless to the average user.
The industry needs to adopt a more user-centric approach to nomenclature. Simplified, intuitive, and descriptive names can greatly enhance user experience.
An Easier Way to Discover Capabilities
Beyond the confusing names, discovering what a specific AI model can actually do is another major hurdle. Often, the capabilities are buried deep within technical documentation. It is compounded by AI models' sheer diversity and specialized functions. A simple name alone might not convey the full spectrum of an AI model’s capabilities.
Thankfully, the AI tools that leverage these models add a small description to specify the use case or their capability—Google, for example, specifies thatGemini 2.0 Flash Thinkingmodel uses advanced reasoning while2.0 Prois best for complex tasks. It’s not ideal, but there’s some help.
Instead of relying on technical terms, model names should reflect their primary function or capabilities. If acronyms are necessary, they should be chosen carefully to ensure they are easy to remember and pronounce. Additionally, clear and concise version numbers should be used to indicate updates and improvements.
Moreover, AI models could be categorized with names that convey their primary function or unique feature, such as “Conversational Bot,” “Text Summarizer,” or “Image Recognizer.” Such clarity would demystify AI technology. This approach will streamline the discovery process, enabling you toquickly identify the most appropriate AI models and tools for your taskswithout sifting through a labyrinth of cryptic names and descriptions.
That said, most language models have diverse capabilities and can do much more than a single task. So, this approach might not be ideal for advanced large language models.
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The current state of AI model names can be perplexing. A shift towards simpler nomenclature and improved discovery methods can greatly enhance user experience and make cutting-edge technology more accessible to everyone. Until then, staying informed, leveraging community resources, and experimenting with different models can help users navigate the intricate world of AI.
