大模型更新信念更符合贝叶斯定理，说明其推理更一致。
Are LLM Belief Updates Consistent with Bayes' Theorem?
- 用贝叶斯一致性系数衡量模型信念更新是否符合贝叶斯定理。
- 参数越多、训练数据越丰富，模型信念更新越符合贝叶斯定理。
- 适合关注大模型推理机制与可信度评估的研究者。
大型语言模型在面对上下文证据时，是否会更一致地更新其对命题的信念？为此，我们提出了贝叶斯一致性系数（BCC）指标，并构建了用于测量BCC的数据集。我们在五个模型家族中测试了多个仅预训练的语言模型的BCC值，对比了模型参数量、训练数据量以及在常见基准上的得分。结果支持我们的假设：更大的、更强大的预训练语言模型所分配的置信度更符合贝叶斯定理。这一发现对理解与治理大模型具有重要意义。
原文摘要 · Abstract (English)
Do larger and more capable language models learn to update their "beliefs" about propositions more consistently with Bayes' theorem when presented with evidence in-context? To test this, we formulate a Bayesian Coherence Coefficient (BCC) metric and generate a dataset with which to measure the BCC. We measure BCC for multiple pre-trained-only language models across five model families, comparing against the number of model parameters, the amount of training data, and model scores on common benchmarks. Our results provide evidence for our hypothesis that larger and more capable pre-trained language models assign credences that are more coherent with Bayes' theorem. These results have important implications for our understanding and governance of LLMs.
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