Basis transformers beat fine-tuned LLMs on 34 multi-task tabular regression datasets while using five times fewer parameters and no data preprocessing.
Bayesian Multitask Learning with Latent Hierarchies
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abstract
We learn multiple hypotheses for related tasks under a latent hierarchical relationship between tasks. We exploit the intuition that for domain adaptation, we wish to share classifier structure, but for multitask learning, we wish to share covariance structure. Our hierarchical model is seen to subsume several previously proposed multitask learning models and performs well on three distinct real-world data sets.
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Basis Transformers for Multi-Task Tabular Regression
Basis transformers beat fine-tuned LLMs on 34 multi-task tabular regression datasets while using five times fewer parameters and no data preprocessing.