DigiNews

Summary

The article argues that increasing model size and data does not linearly translate to better performance or reliability. It highlights real-world hallucination rates across several large models, showing that bigger models can be less trustworthy, and advocates focusing on the trilemma of raw capability, uncertainty calibration, and computational efficiency. The piece uses a Python prompt test and cited benchmarks to illustrate that smaller or differently engineered models can outperform giants on specific tasks.