Confirming Our Biases? Evaluating the Capabilities, Risks, and Societal Impact of Large Language Models
It is well established that large language models (LLMs) are sensitive to prompt framing, reflecting patterns in their training data or prior prompts. In this study, we investigate the extent to which LLMs reinforce users biases expressed in the prompts and examine the boundary between implicit framing effects and explicit prompt manipulation. Specifically, we evaluate how susceptible LLMs are to direct and suggestive prompts that encourage models to support or challenge particular positions. We evaluate six LLMs using 160 distinct prompts spanning ten topics across opinion-based and factual domains. The prompts systematically vary in prompting strategy, support versus challenge instructi...
| Authors | Mudar Adas, Polina Tsvilodub, Michael Franke et al. |
|---|---|
| Primary category | cs.CL |
| Published | 7 August 2026 |
| Last updated at source | 7 August 2026 |
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