Unverified50% confidenceFactExact time
The second term (1-M/N)(1-1/K) of the error rate lower bound corresponds to the traditional coverage problem, where the model can only guess randomly for facts that never appeared during training, with a correct-guess probability of 1/K.
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9/14/2026
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UnverifiedThe first term (M/N)δ*(B/M) of the error rate lower bound describes the compression distortion of facts that the model has seen but stored inaccurately due to limited memory.70% similarUnverifiedNaïve post-training quantization causes individual weight errors to accumulate and amplify across multiple layers—a phenomenon known as compounding error—and at 2-bit or 1-bit precision the model structure collapses68% similarUnverified分布偏移意味着训练集与测试集之间的KL散度不为零，即两个分布之间存在可度量的信息差距68% similarUnverifiedWith only a relatively small amount of targeted training data, it is possible to make a large language model give entirely incorrect answers to clearly stated facts68% similar
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