Meta 新论文提出以视角不变性检验大模型事实真伪
Meta 新论文指出，用似然概率判断大模型输出真伪存在根本缺陷：模型学到的是文本中的常见表述，而非事实本身，被反复提及的谬误反而更“可信”。论文借鉴物理与哲学思路，提出事实应在视角变换下保持不变，即让模型从多个视角（如三位医学专家）作答，只保留各答案的共识部分，若无共识则弃答。该论文未开展实验，仅以构造示例论证，例如在单个二分类例子上，损失 0.67 对应 100% 准确率，而损失 0.71 却对应 0% 准确率。
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Rohan Paul (@rohanpaul_ai) · 2026-11-14 21
Such a simple yet powerful idea in new Meta paper.
Says that likelihood is the wrong test of truth for LLMs, and that answers which stay the same across contexts deserve more trust.
that LLMs are trained to predict the most likely next token, so they learn what is common in text, not what is true. A falsehood repeated often in training data becomes probable, while rare true events get discounted.
Drawing on physics and philosophy, the paper treats a fact as real if it survives a change of viewpoint. The model answers from several viewpoints, such as 3 medical specialists, keeps what every answer shares, and abstains if nothing is shared.
The paper runs no experiments, so its proof is worked examples. On a single binary example, a loss of 0.67 means 100% accuracy, while a nearly equal loss of 0.71 means 0%.
Before trusting a confident answer, check whether it survives changes in persona, framing, or language.
