@inproceedings{pawar-etal-2026-communicative,
title = "Not What, But How: A Communicative Audit of {LLM} Response Framing",
author = "Pawar, Siddhesh Milind and
Masud, Sarah and
Yoo, Haneul and
Oh, Alice and
Augenstein, Isabelle",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.emnlp-main.1124/",
pages = "25732--25765",
ISBN = "979-8-89176-459-0",
abstract = "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 a 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."
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<abstract>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 a 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.</abstract>
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%0 Conference Proceedings
%T Not What, But How: A Communicative Audit of LLM Response Framing
%A Pawar, Siddhesh Milind
%A Masud, Sarah
%A Yoo, Haneul
%A Oh, Alice
%A Augenstein, Isabelle
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F pawar-etal-2026-communicative
%X 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 a 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.
%U https://aclanthology.org/2026.emnlp-main.1124/
%P 25732-25765
Markdown (Informal)
[Not What, But How: A Communicative Audit of LLM Response Framing](https://aclanthology.org/2026.emnlp-main.1124/) (Pawar et al., EMNLP 2026)
ACL
- Siddhesh Milind Pawar, Sarah Masud, Haneul Yoo, Alice Oh, and Isabelle Augenstein. 2026. Not What, But How: A Communicative Audit of LLM Response Framing. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 25732–25765, Budapest, Hungary. Association for Computational Linguistics.