Analyzing and Correcting Benevolence Bias in Large Language Models
Large language models (LLMs) are increasingly used as stand-ins for human respondents, from opinion polls and simulated survey participants to agent-based social simulations. These uses rest on one assumption: that conditioning a model on who a person is yields answers resembling those of real people from that group. Here we identify and measure benevolence bias, a small but consistent tendency for aligned LLMs to lean toward the kinder, safer, more socially approved answer on value-laden survey questions. Across 18 widely used models, four social-science datasets (ANES, GSS, WVS, and a cross-cultural prospect-theory replication) and six psychological categories, we find that the bias is ...
| Authors | Yuanzi Li, Junhao Wang, Minghui Liu et al. |
|---|---|
| Primary category | cs.HC |
| Published | 26 July 2026 |
| Last updated at source | 26 July 2026 |
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