Generalization of the Alignment Tax to Closed-Source GPT-Class Models and Other Domains

Ascertain whether the alignment tax—response homogenization that degrades sampling-based uncertainty estimation—generalizes to closed-source GPT-class language models and to additional domains such as code and dialogue.

Background

The study’s empirical evidence covers open-source models between 3B and 14B parameters across several datasets, showing DPO-driven response homogenization and its impact on sampling-based uncertainty.

The authors explicitly note that generalization to closed-source GPT-class systems and other domains (e.g., code, dialogue) remains unconfirmed, making this a critical open question for broader applicability and deployment.