Entropiformer

Entropiformer is a family of transformer-based models and training methods that incorporate entropy, an information-theoretic measure of uncertainty, as a core design element. The approach seeks to improve calibration, robustness to distributional shift, and data efficiency in tasks such as language modeling and machine translation.

Most entropiformers extend standard transformer architectures by adding entropy-aware components. These may include (1) an entropy regularization term applied to the model’s predicted output distribution, (2) entropy penalties or constraints on attention weights to discourage over-concentrated or brittle attention, and (3) optional latent variables with entropy-aligned priors to capture uncertainty in representations.

Training combines the usual objective, such as cross-entropy for supervised tasks or maximum likelihood for language models, with entropy-based terms. The entropy term can be weighted to encourage either reduced or increased uncertainty depending on the aim; for example, minimizing negative entropy to push confident predictions or maximizing entropy to promote calibrated probabilities in uncertain cases. Regularization can be applied at the token level, sequence level, or to attention distributions.

Empirical goals include improved calibration of prediction probabilities, better performance on out-of-distribution data, and smoother generalization when data are scarce. Entropiformers are also studied for active learning, where high-entropy samples may be selected for labeling.

Challenges include tuning the entropy weight, potential conflicts with likelihood objectives, training instability, and higher computational overhead due to additional regularization terms or latent-variable components.

Entropiformer remains a developing area in the field of deep learning, with research exploring optimal configurations, theoretical properties, and domain-specific benefits.