斯坦福发了篇ICML论文，说LLMs猜别的模型比猜事实还准，但一起翻车时投票也救不了。想看懂它们为啥集体犯傻？看这篇。
斯坦福团队在ICML 2026论文中发现，LLMs预测其他模型输出的准确率高于预测事实。当所有模型都预测错误时，投票法无法恢复正确答案。自我一致性（多次采样并验证）在数学和代码领域效果良好，但在无验证器的领域无法扩展真实性。论文标题为'Truthfulness Does Not Scale Like Reasoning'。
LLMs are better at predicting what other models will say than what’s actually true. When they’re wro...
LLMs are better at predicting what other models will say than what’s actually true. When they’re wrong, they’re wrong together; so polling can’t recover the truth. Check out this paper at ICML! 🇰🇷 Jessica Chudnovsky ✈️ ICML 2026 @jchudnov Pass @k and self-consistency work great for math and code; sample more and verify. So we asked: can the same trick scale truthfulness in domains with no verifier? The answer was no. Excited to share our #ICML2026 conference paper: Truthfulness Does Not Scale Like Reasoning. arxiv.org/pdf/2603.06612 . I’ll be at ICML in Seoul to present it! Co-led by @JoshuaK92829 and @yegordb 🔗 View Quoted Tweet 💬 1 🔄 6 ❤️ 57 👀 11016 📊 11 ⚡