While work on LLM-based regression has largely focused on scalar prediction, multi-output text regression remains less explored.
We present DualView, a parameter-efficient approach to leverage LLMs for multi-output text regression.
method first extracts broad and output-specific representations from a frozen LLM, then uses a dual-branch architecture to produce the full score vector.
Across five heterogeneous regression tasks and eight LLMs (up to 122B), DualView outperforms seven supervised regression baselines while requiring 82--1842 times fewer fitted parameters than the three strongest baselines.
These improvements extend across individual output dimensions and persist even with limited training data.
Notably, DualView using a smaller open-weight LLM can outperform unsupervised prediction with Gemini 3.1 Pro.
Controlled ablations show that broad and output-specific evidence provide complementary predictive benefits, while lightweight shared predictors achieve strong performance with fewer trainable parameters.