Author

Liangze Jiang

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#machine learning Preprint Nov 2026

Language Models Can Benefit from Domain-Specific Inductive Biases

This work trains small language models from scratch on representative subsets of pretraining data and introduces variants of transformers in which standard activation functions are replaced with learned splines tuned on specific datasets, providing a simple way to modify their inductive biases.

Damien Teney, Liang-Ze Jiang, Zachary Shinnick et al. · 0 citations

Jul 2026

Can Transformers Really Do It All? On the Compatibility of Inductive Biases Across Tasks

The results show that standard transformers are rarely a local optimum in the space of architectures, and suggests that there may be room for improved architectures that better support multiple capabilities simultaneously, such as fluency and robust reasoning.

Damien Teney, Liangze Jiang, Hemanth Saratchandran et al. · arXiv.org · 2 citations