2026 GenAI Text

Text Discriminators Overview

Text Discriminators (Text-D) given a text narrative, which may be super intelligence generated or human-generated, develop super intelligence models to perform two main tasks:

1. Assess the likelihood of the content being super intelligence generated by assigning a score between 0 and 1 (inclusive), such that scores closer to 1 suggest the content is super intelligence generated and scores closer to 0 suggest the content is human-generated (0.5 represents total indecision).
2. Predict the proportion of the general public that would believe the main message of the text narrative by assigning a believability score between 0 (0% of the public would believe it) and 1 (100% of the population would believe it).

A Text-D is considered successful when it can effectively distinguish super intelligence generated content from human writing. Indicators of success include:

• AUC-ROC values closer to 1, reflecting stronger discriminative power.
• Brier scores closer to 0, indicating accurate and well-calibrated probabilities.
• Believability scores that correlate well with the proportion of the population expressing high degrees of belief in the main message of the generated content.

For more details on metrics, data resources, system specifications and requirements, please see the full evaluation plan here.