aiqbench analysis
This research challenges the standard practice of using single demographic cues like names to evaluate LLM bias, revealing that such methods produce unstable and inconsistent results regarding personalization and fairness. By analyzing over 14.8 million prompts, the study demonstrates that model responses depend fundamentally on specific linguistic signals rather than stable demographic categories, necessitating a shift toward multi-cue, mechanism-aware evaluation frameworks.
Different Demographic Cues Yield Inconsistent Conclusions About LLM Personalization and Bias
arXiv:2601.18486v3 Announce Type: replace Abstract: Demographic cue-based evaluation is widely used to study how large language models (LLMs) adapt their responses to signaled demographic attributes within and across groups. This approach typically relies on a single cue (e.g., names) as a proxy for group membership, implicitly treating different cues as interchangeable operationalizations of a single underlying identity-conditioned behavior. We test this assumption in realistic advice-seeking interactions spanning 14.8 million prompts, focusing on race and gender in a U.S. context. We find that cues for the same group induce only partially overlapping changes in model responses, yielding inconsistent conclusions about personalization, while bias conclusions are unstable, with both magnitude and direction of group differences varying across cues. We further show that these inconsistencies reflect differences in cue-group association strength and linguistic features bundled within cues that shape model responses. Together, our findings suggest that demographic conditioning in LLMs is not a cue-invariant category-level parameter but depends fundamentally on how identity is cued, reflecting responses to linguistic signals rather than stable demographic categories. We therefore call for multi-cue, mechanism-aware evaluations as a foundation for robust and interpretable claims about demographic variation in LLM responses.
Abstract:Demographic cue-based evaluation is widely used to study how large language models (LLMs) adapt their responses to signaled demographic attributes within and across groups. This approach typically relies on a single cue (e.g., names) as a proxy for group membership, implicitly treating different cues as interchangeable operationalizations of a single underlying identity-conditioned behavior. We test this assumption in realistic advice-seeking interactions spanning 14.8 million prompts, focusing on race and gender in a U.S. context. We find that cues for the same group induce only partially overlapping changes in model responses, yielding inconsistent conclusions about personalization, while bias conclusions are unstable, with both magnitude and direction of group differences varying across cues. We further show that these inconsistencies reflect differences in cue-group association strength and linguistic features bundled within cues that shape model responses. Together, our findings suggest that demographic conditioning in LLMs is not a cue-invariant category-level parameter but depends fundamentally on how identity is cued, reflecting responses to linguistic signals rather than stable demographic categories. We therefore call for multi-cue, mechanism-aware evaluations as a foundation for robust and interpretable claims about demographic variation in LLM responses.
Computation and Language (cs.CL); Computers and Society (cs.CY)
arXiv:2601.18486 [cs.CL]
(or arXiv:2601.18486v3 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2601.18486 Focus to learn more arXiv-issued DOI via DataCite
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Full text under the publisher's open licence · 284 words
