Not What, But How: A Communicative Audit of LLM Response Framing
For researchers and developers evaluating LLMs on subjective queries, this work provides a multi-dimensional audit tool that goes beyond factual accuracy, though it is an incremental extension of existing evaluation methods.
The paper introduces FRANZ, an automated framework for auditing how LLMs frame responses to subjective cultural questions across four dimensions. Using a new corpus of 376k questions, they find that LLMs differ significantly in their use of response characteristics and that insider positioning and anthropomorphism are positively coupled, varying by country.
Large language models (LLMs) are being increasingly used to answer subjective, information-seeking questions, where users are sensitive to how responses are communicated, not just whether the answers are correct. Existing LLM evaluations for subjective cultural queries largely focus on factual correctness, ignoring how the response is framed. To this end, we introduce FRANZ, an automated FRAmework for respoNse characteriZation to conduct communicative audit of LLM responses along four dimensions: cultural positioning, use of generalizing language, anthropomorphic cues, and adherence to conversational maxims. To enable this evaluation, we contribute SQUARE - a corpus of 376k subjective questions sourced from 57 subreddits, and mapped to 7 countries and 19 question categories. We demonstrate FRANZ's applicability by scoring responses from three open-weight LLMs. We observe that LLMs show statistically significant differences in the frequency with which they employ each response characteristic. Unlike single-dimensional audits, FRANZ reveals that insider positioning and anthropomorphism are positively coupled, with the degree of coupling varying by country, providing a diagnostic lens for identifying framing divergences.