PoliticsBench: Benchmarking Political Values in Large Language Models with Multi-Turn Roleplay
arXiv:2603.23841v2 Announce Type: replace-cross Abstract: While Large Language Models (LLMs) are increasingly used as primary sources of information, their potential for political bias may impact their objectivity. Existing benchmarks of LLM social bias primarily evaluate demographic stereotypes, and when political bias is measured, it is done so at a coarse level, overlooking the values that shape sociopolitical reasoning. We introduce PoliticsBench, a multi-stage roleplay benchmark for evaluating fine-grained value expression in LLMs. Across twenty evolving scenarios, models articulate trade
The increasing deployment of LLMs as primary information sources necessitates a deeper understanding and mitigation of their inherent biases, especially as their societal influence grows.
Understanding and benchmarking political values in LLMs is crucial for ensuring their objectivity, trustworthiness, and preventing the inadvertent propagation of specific ideologies at scale.
The introduction of 'PoliticsBench' provides a more fine-grained, robust methodology for identifying and evaluating political biases beyond simple demographic stereotypes, offering a clearer picture of LLM value systems.
- · AI ethics researchers
- · Organizations developing less biased LLMs
- · Policy makers
- · LLM developers ignoring bias mitigation
- · Users relying on unchallenged LLM outputs
Increased scrutiny and improved methods for detecting political bias within large language models.
Development of LLMs specifically engineered to be more objective or transparent about their inherent political leanings.
Potential for new regulatory frameworks or industry standards requiring explicit disclosure or benchmarking of LLM political biases.
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Read at arXiv cs.AI