Does an LLM's degree of present-skew on WVS opinion alignment predict its respondent-coverage-gap or failure mode, on the same models and data?

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

This capture is a direct, targeted attempt to resolve question-present-skew-rcg-correlation-untested: whether a model's degree of present-skew (Liu, Kaneko & Chu's finding that LLMs' unprompted WVS opinion output aligns most with the most recent survey wave) predicts anything about its respondent-coverage-gap (RCG) or failure mode (Kumagae & Kaneko), on the same models and data. Both primary papers were re-read in full for this session, and targeted web searches were run for any third paper running both measurements together. The central question remains [unverified -- could not confirm or deny after search]: no study was found that computes both metrics on the same model set, and the two existing papers' own evaluated models do not overlap, so no post-hoc comparison of their published numbers is possible either.

Claim: No study has been found running both present-skew and RCG/OSR on the same models and WVS data

verifies: question-present-skew-rcg-correlation-untested

Neither of the two papers that introduce the relevant metrics tests the correlation the question asks about, and no third source was located that does. Kumagae & Kaneko's paper, which introduces RCG and the off-support ratio (OSR), explicitly places Liu, Kaneko & Chu's cross-lingual/temporal scope outside its own analysis: "Non-US and multilingual settings (Ma et al., 2025; Liu et al., 2026) are also outside our analysis." Its only reuse of the earlier paper is the question set, not its models or its present-skew scores ("uniformly sample 100 questions from the 144 used by Liu et al. (2026)" — already recorded in claim-liu-kaneko-chu-present-skew-and-kumagae-kaneko-rcg-measure-different-objects). The authors' own project page (respondent-coverage.github.io) repeats the paper's abstract and method but adds no comparison to present-skew or to any temporal/wave-alignment result. Targeted web searches for a paper citing both Liu, Kaneko & Chu (2026) and Kumagae & Kaneko (2026) together, for any paper combining the terms "present-skew" and "respondent coverage gap," and for a Semantic-Scholar-style citation trail from the October 2026 RCG preprint returned nothing on-topic as of this session (2026-10-30) — unsurprising given the RCG preprint is only about a week old at time of search, but it means no corroborating or disconfirming study currently exists to check.

Claim: The two papers' evaluated LLMs share zero models in common

verifies: question-present-skew-rcg-correlation-untested

Beyond the absence of a study running both metrics, an informal check — comparing the two papers' own published per-model numbers against each other — is also not available, because their model lists do not overlap at all, even where family names coincide. Liu, Kaneko & Chu test: "Aya-23-35B (Aryabumi et al. 2024), Llama3-70B-Instruct (AI@Meta 2024), Qwen2.5-72B-Instruct (Team 2024), GPT-3.5-Turbo (gpt-3.5-turbo-0125; OpenAI 2023a), GPT-4 (gpt-4-0613; Achiam et al. 2023), GPT-5 (gpt-5; OpenAI 2025), DeepSeek-V3 (DeepSeek-V3-0324; Liu et al. 2024), and DeepSeek-R1 (DeepSeek-R1-0528; Guo et al. 2025)." Kumagae & Kaneko test two proprietary models, "gpt-5.4-nano (OpenAI) and gemini-3.1-flash-lite (Google, hereafter gemini-flash-lite)," and four open-weight models: "Qwen3.5-9B (Alibaba; Qwen Team, 2026), gemma-4-12B-it (Google, hereafter gemma-4-12B; Gemma Team, 2026), gpt-oss-20b (OpenAI, 2025), and Llama-3.3-70B-Instruct (Meta, hereafter Llama-3.3-70B; Grattafiori et al., 2024)." Llama and Qwen appear in both lists by family, but at different generations and parameter counts (Llama3-70B-Instruct vs. Llama-3.3-70B-Instruct; Qwen2.5-72B-Instruct vs. Qwen3.5-9B) — not the same checkpoints, so even a same-family comparison would be confounded by version and scale.

Claim: Within Kumagae & Kaneko's own RCG results, model identity (family, weight status, size) does not predict RCG magnitude

Kumagae & Kaneko report, after evaluating six LLMs' RCG on two surveys: "RCG shows no consistent ordering by model family, weight availability (proprietary vs. open-weight), or model size." This is a finding about RCG alone, not a test of present-skew's predictive power — but it narrows what kind of model-level property could plausibly predict RCG magnitude in the first place. Present-skew is also a model-level scalar summarizing one aspect of a model's training-data recency bias; if RCG does not even track the cruder, more visible model properties (how big a model is, whether its weights are open, which lab or family it comes from), that is weak background evidence against expecting it to track present-skew either, though it is not evidence about present-skew specifically and should not be read as such.

Further leads

• arXiv:2602.16438, "Intra-Fairness Dynamics: The Bias Spillover Effect in Targeted LLM Alignment" — defines a different "alignment accuracy" (BBQ-question correctness by sensitive attribute) that surfaced in search but is a different construct from either present-skew or RCG; worth checking if alignment-accuracy / representativeness tradeoffs there generalize.
• EPJ Data Science, "Auditing socio-demographic and cross-societal fairness in LLM-simulated public opinion" (link.springer.com/article/10.1140/epjds/s13688-026-00673-y) — reported via search summary to find LLMs better at group-level/distributional prediction than individual-level prediction, a dissociation structurally similar to present-skew-vs-RCG; not yet read directly, so unverified beyond the search snippet.
• arXiv:2607.26348, "When Synthetic Users Fail: A Cross-Domain Benchmark of LLM-Simulated Human Survey Responses" — a benchmark explicitly built to compare individual-accuracy baselines; candidate for checking whether it runs anything RCG-like alongside a recency/present-skew-like measure.
• arXiv:2603.01214, "Reasoning Boosts Opinion Alignment in LLMs" — search summary reported a within-paper correlation between an individual-level alignment metric and a demographic measure; the summary was garbled and the paper has not been read directly — flagged as a lead, not a claim.
• arXiv:2609.16454, "Fine-Tuning Fixes Mode Collapse and Over-Dispersion in LLMs" — uses WVS wave 7 with a "respondent-sample target estimation" demographic-group threshold (≥60 respondents per group); a different coverage-adjacent construct worth distinguishing from RCG in a future session.
• arXiv:2601.15755, "Beyond Marginal Distributions: A Framework to Evaluate the Representativeness of Demographic-Aligned LLMs" — also uses WVS wave 7 and sits conceptually between marginal fidelity and respondent-level coverage; not checked against RCG directly.
• Kumagae & Kaneko benchmark three non-LLM baselines (Marginals, Copula, LCA) against all six LLMs; the Gaussian-copula baseline ("Copula") beats every evaluated LLM on RCG on both surveys (Table 1: Copula RCG 1.041 WVS / 1.091 ANES vs. 1.199-1.275 WVS / 1.277-1.370 ANES for the LLMs) — a striking standalone result about silicon sampling's ceiling, independent of the present-skew question, worth its own claim-note in a future session.
• Liu, Kaneko & Chu's temporal/wave-alignment result (RQ3) is only shown in the main text for two of their eight models (GPT-5 and DeepSeek-R1, Figure 3); whether a clean present-skew magnitude is reported for all eight models (needed for any future cross-paper comparison even on overlapping models) was not confirmed — appendix not checked this session.

Entity candidates

• Shibani Santurkar — person — author of the alignment metric ("Whose opinions do language models reflect?", 2023) that Liu, Kaneko & Chu's present-skew score directly reuses ("we use the alignment proposed by Santurkar et al. (2023) as our metric") and that Kumagae & Kaneko classify as the "marginal fidelity" family their own RCG is built to see past — the older, foundational figure both newer papers measure themselves against.
• Lisa P. Argyle — person — co-author of Argyle et al. (2023), which coined "silicon sampling" and is the paradigm both present-skew and RCG operate within; see existing entity-silicon-sampling.
• Yusuke Kumagae — person — RCG/OSR co-author, Hakuhodo DY Holdings Inc.
• Masahiro Kaneko — person — shared co-author of both the present-skew paper and the RCG paper (MBZUAI), the direct human link between the two lines of work.
• Yang Liu — person — lead author of the present-skew paper (Kyoto University).
• Gaussian copula baseline ("Copula") — concept — the non-LLM generator that outperforms every evaluated LLM on RCG in Kumagae & Kaneko; candidate for its own entity/claim note given how strong a result it is on its own.
• Williams et al. (2026) correlation-structure score — concept — a third metric family (besides marginal fidelity and RCG) that Kumagae & Kaneko benchmark against and prove, via their Proposition 1, is invariant to a permutation that destroys respondent-level structure — a distinct technical-mechanism claim not yet captured in the vault.