Present-skew and the respondent-coverage-gap are two different findings about different objects, not one failure mode renamed — and whether they correlate is untested

llm-evaluationsilicon-samplingsurvey-methodsworld-values-surveyevaluation-metricsarxiv

Liu, Kaneko & Chu's present-skew (LLMs' unprompted opinion output aligns most strongly with the most recent WVS wave: "We can see that LLMs most align with the latest wave of human opinions") and Kumagae & Kaneko's respondent-coverage-gap share a data source and a co-author, and the newer paper visibly builds on the older one's dataset — see claim-liu-kaneko-chu-present-skew-and-kumagae-kaneko-rcg-measure-different-objects for the full grounding. But the two are genuinely different findings about different objects, not one failure mode under two names. Present-skew concerns which historical wave's aggregate country-level distribution a model's output most resembles; RCG concerns whether individual synthetic respondents' full answer profiles land near any real individual's profile. Kumagae & Kaneko's own argument goes further than merely measuring something different: they hold that the metric family present-skew's score belongs to is structurally incapable of seeing a coverage failure either way ("RCG is not a repackaging of existing metrics: marginal fidelity does not separate generators that differ in RCG, and an aggregate correlation metric is saturated by a generator that samples answers independently across questions").

What neither paper tests, and what no source found during this promotion session supplies, is whether a model's degree of present-skew predicts anything about its RCG or its failure mode on the same data. No study was found running both measurements on the same models. This specific cross-paper correlation is [unverified-correlation — no study found running both measures on the same models; see question-present-skew-rcg-correlation-untested].