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: "We experiment with seven instruction-tuned multilingual LLMs: Aya-23-35B (Aryabumi et al. 2024), Llama3-70B-Instruct (AI@Meta 2024), Qwen2.5-72B-Instruct (Team 2024), GPT-3.5-Turbo (OpenAI 2023b, gpt-3.5-turbo-0125;), GPT-4 (Achiam et al. 2023, gpt-4-0613;), GPT-5 (OpenAI 2025, gpt-5;), DeepSeek-V3 (Liu et al. 2024, DeepSeek-V3-0324;), and DeepSeek-R1 (Guo et al. 2025, DeepSeek-R1-0528;)." (The sentence's own count says "seven" while it names eight models; the paper's RQ2 results table carries eight model columns, so the count, not the list, appears to be the leftover.) Kumagae & Kaneko, evaluating respondent-coverage-gap (RCG), test six: "We use two proprietary models: gpt-5.4-nano (OpenAI) and gemini-3.1-flash-lite (Google, hereafter gemini-flash-lite)." and "We use 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)."

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.