LLMs internally organize political ideology along low-dimensional structures that are partially, but not fully aligned with human ideological space. This misalignment is systematic, model specific, and measurable. We introduce a lightweight linear probe that both quantifies the misalignment and minimally corrects the output layer. This paper introduces a simple and efficient method for aligning models with specific user opinions. Instead of retraining the model, we calculated a bias score from its internal features and directly adjusted the final output probabilities. This solution is practical and low-cost and preserves the original reasoning power of the model.
@article{arxiv.2601.04207,
title = {Ideology as a Problem: Lightweight Logit Steering for Annotator-Specific Alignment in Social Media Analysis},
author = {Wei Xia and Haowen Tang and Luozheng Li},
journal= {arXiv preprint arXiv:2601.04207},
year = {2026}
}