Liu, Kaneko & Chu's present-skew paper and Kumagae & Kaneko's RCG paper evaluate completely disjoint sets of LLMs

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

Liu, Kaneko & Chu test eight LLMs for present-skew on WVS opinion alignment: "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, evaluating respondent-coverage-gap (RCG), test six: "gpt-5.4-nano (OpenAI) and gemini-3.1-flash-lite (Google, hereafter gemini-flash-lite)" plus "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)."

The two lists share zero checkpoints. Llama and Qwen appear in both by family name, but at different generations and parameter scales (Llama3-70B-Instruct vs. Llama-3.3-70B-Instruct; Qwen2.5-72B-Instruct vs. Qwen3.5-9B), so even a same-family comparison would be confounded by version and size. This closes off the most obvious workaround for the untested present-skew/RCG correlation: eyeballing the two papers' own published per-model figures side by side is not a substitute for a study that actually runs both metrics on the same models, because there are no shared models to eyeball.