A New Standard for Evaluating LLM Accuracy
Moving beyond basic metrics. We propose a framework using **symbolic codes** to capture nuanced understanding, user satisfaction, and contextual awareness in AI responses.
Why Symbolic Codes?
Capture Nuance
Simple pass/fail or accuracy scores miss the point. Codes can represent states like "partially correct," "correct but unhelpful," or "misunderstood intent."
Objective & Standardized
Create a clear, machine-readable standard for model evaluation that is less ambiguous than open-ended human scoring.
Actionable Feedback
These codes provide a direct signal for fine-tuning, RLHF, and identifying systemic model weaknesses, making improvement cycles faster and more targeted.
Proof-of-Concept: `statuscodes10`
We've released a small, experimental dataset on Hugging Face to demonstrate this concept.
Join the Initiative
This is a community-driven effort to build a better standard for AI evaluation. If you're a researcher, developer, or enthusiast, we need your contribution.