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Evidence record 14458 · automatically gathered

Auditing Alignment Controllability in LLMs via Political Axes

Political audits of large language models (LLMs) usually reduce each to one point on a political compass. But that resting point barely matters in deployment: a model must land somewhere, and what counts is how far, and in which directions, its answers can be steered. That steering runs through the system prompt: the personalization layer a platform sets, or one induced from a user's history, not necessarily written by hand. We run a dispersion-first stress test of prompt-based controllability a

Record details

Published: 26 July 2026
Source: arXiv cs.CL (ethics-relevant NLP)
Category: Research
Topics: Safety & alignment · Transparency
Retrieved: 29 July 2026

source-onlyevidence status

These records share source-supplied organisations, an exact publisher byline, automatic topics or regions. The reason is shown on every link; related does not mean supporting, agreeing with or verifying this record.

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How to cite this record

ethics.ai (26 July 2026), “Auditing Alignment Controllability in LLMs via Political Axes,” evidence record 14458, https://ethics.ai/record/14458 (originally published by arXiv cs.CL (ethics-relevant NLP)).

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