{"ID":2831709,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2601.04207","arxiv_id":"2601.04207","title":"Ideology as a Problem: Lightweight Logit Steering for Annotator-Specific Alignment in Social Media Analysis","abstract":"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.","short_abstract":"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 o...","url_abs":"https://arxiv.org/abs/2601.04207","url_pdf":"https://arxiv.org/pdf/2601.04207v1","authors":"[\"Wei Xia\",\"Haowen Tang\",\"Luozheng Li\"]","published":"2025-12-08T14:07:44Z","proceeding":"cs.CL","tasks":"[\"cs.CL\",\"cs.AI\",\"cs.SI\"]","methods":"[\"Large Language Model\",\"Generative Adversarial Network\"]","has_code":false}