The rapid advancement of Large Language Models (LLMs) has sparked intense debate regarding their ability to perceive and interpret complex socio-political landscapes. In this study, we undertake an exploration of decision-making processes and inherent biases within LLMs, exemplified by ChatGPT, specifically contextualizing our analysis within political debates. We aim not to critique or validate LLMs' values, but rather to discern how they interpret and adjudicate "good arguments." By applying Activity Dependency Networks (ADNs), we extract the LLMs' implicit criteria for such assessments and illustrate how normative values influence these perceptions. We discuss the consequences of our findings for human-AI alignment and bias mitigation. Our code and data at https://github.com/david-jenny/LLM-Political-Study.

翻译：大语言模型（LLMs）的快速发展引发了关于其感知和解读复杂社会政治景观能力的激烈辩论。在本研究中，我们以ChatGPT为例，对LLMs的决策过程及固有偏见展开探索，并将分析具体置于政治辩论的语境中。我们的目标并非批判或验证LLMs的价值取向，而是揭示它们如何解读和判定"良好论点"。通过应用活动依赖网络（ADNs），我们提取了LLMs对此类评估的隐含标准，并阐明规范性价值观如何影响这些认知。我们讨论了研究结果对人类与AI对齐及偏见缓解的启示。我们的代码和数据详见https://github.com/david-jenny/LLM-Political-Study。