用轻量方法纠正大模型在社交媒体中的意识形态偏差。
Ideology as a Problem: Lightweight Logit Steering for Annotator-Specific Alignment in Social Media Analysis
- 通过内部特征计算偏见分数，直接调整输出概率。
- 无需微调模型，即可对齐特定用户观点，效果可量化。
- 适合需要快速适配用户立场的社交内容分析场景。
大语言模型在内部以低维结构组织政治意识形态，但与人类意识形态空间存在系统性偏差，且因模型而异、可测量。本文提出一种轻量级线性探测器，既能量化该偏差，又能最小化修正输出层。通过分析模型内部特征计算偏见分数，并直接调整最终输出概率，实现不重训练即可对齐特定用户观点。该方法成本低、效率高，同时保持模型原有推理能力。
原文摘要 · Abstract (English)
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.
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