aiqbench analysis
This study audits the cultural alignment of TypeSafe's JEV, a decision-only language model used as a survey respondent or judge, revealing how persona instructions and language choice significantly shift its expressed values. The research demonstrates that while the model can mimic human cultural differences between Saudi and American profiles, it exhibits lower confidence in Arabic and occasionally reverses specific value orientations like long-term orientation. These findings are critical for developers deploying non-generative models in cross-cultural evaluation tasks where bias mitigation is essential.
Calibrated to Whom? Persona and Language Effects on Cultural Values in JEV
arXiv:2609.36399v1 Announce Type: new Abstract: Decision-only language models return a probability for every answer option instead of generating text, which makes them attractive as survey respondents and as judges. We audit the cultural values of one such model, TypeSafe's JEV, with the Values Survey Module 2013. We asked it the 24 items as 12 matched Saudi and 12 matched American personas and without a persona, in English and Arabic, under eight ways of formulating the request (288,000 answers). JEV's answers were highly repeatable (ICC 0.997), and without a persona they resembled those of its own American personas. When the persona was Saudi rather than American, the answers moved in the direction of the human Saudi-US difference, reproducing 87% of its size in English but 62% in Arabic, with long-term orientation reversed. A language cross shows that the smaller difference in Arabic comes from the language of the items, not from the language of the persona description. Age shifted the profiles about as much as nationality, gender shifted them more for Saudi than for American personas, and JEV was less confident in Arabic and for Saudi personas. These patterns held in every request design, although the model never generates text.
Title:Calibrated to Whom? Persona and Language Effects on Cultural Values in JEV
Abstract:Decision-only language models return a probability for every answer option instead of generating text, which makes them attractive as survey respondents and as judges. We audit the cultural values of one such model, TypeSafe's JEV, with the Values Survey Module 2013. We asked it the 24 items as 12 matched Saudi and 12 matched American personas and without a persona, in English and Arabic, under eight ways of formulating the request (288,000 answers). JEV's answers were highly repeatable (ICC 0.997), and without a persona they resembled those of its own American personas. When the persona was Saudi rather than American, the answers moved in the direction of the human Saudi-US difference, reproducing 87% of its size in English but 62% in Arabic, with long-term orientation reversed. A language cross shows that the smaller difference in Arabic comes from the language of the items, not from the language of the persona description. Age shifted the profiles about as much as nationality, gender shifted them more for Saudi than for American personas, and JEV was less confident in Arabic and for Saudi personas. These patterns held in every request design, although the model never generates text.
Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
arXiv:2609.36399 [cs.CL]
(or arXiv:2609.36399v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2609.36399 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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Full text under the publisher's open licence · 304 words
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