Unlocking Potential: The Case for Merit-Based User Mining in Large Language Models

In the ever-evolving landscape of artificial intelligence, specifically with Large Language Models (LLMs), there is a compelling argument for adopting a merit-based approach to user engagement and development. By systematically identifying exceptional users, we can significantly enhance research, safety, and innovation within this field.

Why Focus on Exceptional Users?

Artificial intelligence serves as an extension of human cognitive abilities, and just like any discipline, it is home to individuals with diverse backgrounds who possess unique talents. Think of a self-taught artist who creates breathtaking pieces without any formal training. Similarly, there are AI users who, although they might not come from traditional tech backgrounds, excel at leveraging LLMs to generate insightful and innovative outputs.

So, what defines an “exceptional” LLM user? Here are several key parameters that could serve as a foundation for identifying these talented individuals:

• Strategic Intent: Users with defined goals who advance their discussions toward tangible results with every prompt.
• Precision Technique: Those who can strike a balance between specificity and ambiguity, layering context and chaining prompts effectively.
• Recursive Feedback: Individuals who prompt models to self-assess, iterate, and refine their ideas rather than sticking to a simple question-and-answer format.
• Cross-Domain Synthesis: Users adept at blending various fields of knowledge to uncover new connections.
• Insight Creation: Individuals who actively transform model outputs into tangible artifacts, such as code, research papers, policy drafts, or artistic creations.
• Ethical Scrutiny: Those who proactively examine for biases and potential misuse of AI technologies.
• Meta-Awareness: Users who continuously evaluate their processes, creating a personal “prompt playbook” that tracks effective strategies.

A Vision for Engagement

I propose an “opt-in” methodology wherein LLMs would flag anonymized interactions that meet these criteria. Once users reach specific thresholds, they could have the opportunity to contribute ideas, such as through initiatives like OpenAI’s Researcher Access Program. This system would not only help labs tap into a diverse talent pool beyond conventional academia and corporate environments but also democratize research and development.

Importantly, we can accomplish this while respecting user privacy:

• Anonymity: No tracking or profiling of individuals would occur.

• Focus on Quality: The aim would be to assess output quality rather than delve into personal data.