研究发现 LLM 道德判断缺乏稳定的价值排序
很多人拿道德测试分数判断模型对齐水平，这篇论文直接拆了台：GPT-5.2、Gemini、DeepSeek 等 5 个模型调换选项顺序后答案就变，排序一致性几乎不存在。做模型评估、安全对齐的人应该看看，别再被平均分骗了。
一篇新论文测试了 GPT-5.2、Gemini、DeepSeek 等 5 个 LLM 的道德判断能力，方法是让模型在成对的道德违规情境中选择哪个更糟，并直接从 next-token 输出读取答案概率，检验选择是否符合一致的价值排序。
结果显示，仅调换两个选项的呈现顺序，就使 30 个测试块中的 26 个出现排序变化。更关键的是，对结果取平均后，5 个模型中有 4 个呈现出干净且几乎相同的道德层级，但这种排序与任何单个测试集的实际选择都对不上，说明此前道德测试的高分可能是平均化制造出的假象。
这对依赖道德对齐分数评估模型的研究者提出了方法学警示：评估时必须在单条 prompt 上、且同时使用两种答案顺序检验一致性，而不能依赖聚合后的平均结果。
This paper finds that LLM moral choices often lack a stable ranking of values, and averaging their answers creates a fake, human-like moral hierarchy.
Many studies conclude that LLMs hold sensible moral values because they do well on morality tests. But good answers do not show that a model weighs competing values against each other.
The researchers gave 5 LLMs, including GPT-5.2, Gemini, and DeepSeek, pairs of moral violations and asked which was worse. They read each answer's probability straight from the model's next-token output, then tested whether the choices fit a consistent ranking.
Reversing the order of the 2 options changed the ranking in 26 of 30 test blocks. After averaging, 4 of 5 models showed a clean, nearly identical ranking that matched no individual test set.
Before trusting a model's moral alignment scores, check that its choices hold a consistent ranking on individual prompts and both answer orders, without averaging.
This report is categorized under AITOP's "Papers" desk, focusing on US frontier AI labs and Silicon Valley technology leaders. Within our cross-regional analytical framework, it directly maps to AI developer toolchains, coding agents, and engineering workflows. Key entities and technical topics under continuous tracking include GPT-5.2, Gemini, DeepSeek, AI安全.
Source attribution: curated from rohanpaul_ai (@rohanpaul_ai).
