FramingQA: Does the Question Shape the Answer? Measuring the Compositional Framing Effect
We introduce FramingQA, a benchmark that measures the model sensitivity to question framing across law, medicine, finance, and robotic simulations. Large language models (LLMs) often change their responses to subtle rephrasings that align with an implied stance by users. This can leave users with advice tainted by how they happened to phrase a question rather than by the underlying facts, and the consequences are highly costly in high-stakes domains. Because in the realistic scenarios, both expert practitioners and non-expert users frequently ask LLMs questions containing incomplete or misleading assumptions, models are highly susceptible to those framings. To test this, we inject the fra...
| Authors | Hazel H. Kim, Andrew M. Bean, Guilherme Affonso Ferreira de Camargo et al. |
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| Primary category | cs.CL |
| Published | 7 September 2026 |
| Last updated at source | 4 October 2026 |
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