Semantic Uncertainty: Linguistic Invariances for Uncertainty Estimation in Natural Language Generation

Lorenz Kuhn, Yarin Gal, Sebastian Farquhar

OATML Group, Department of Computer Science, University of Oxfor

语义不确定性：自然语言生成中不确定性估计的语言不变性

要点：

1.介绍了一种测量大型语言模型中不确定性的方法。对于像问题回答这样的任务，了解何时可以信任基础模型的自然语言输出是至关重要的。

2.文章发现，由于“语义对等”，测量自然语言中的不确定性具有挑战性——不同的句子可能意味着相同的事情。

3.克服这些挑战，我们引入了语义熵——一种结合了由共享意义产生的语言不变量的熵。文章的方法是无监督的，只使用一个模型，不需要修改现成的语言模型。

一句话总结：

在综合消融研究中，表明语义熵比可比基线更能预测问答数据集的模型准确性。

We introduce a method to measure uncertainty in large language models. For tasks like question answering, it is essential to know when we can trust the natural language outputs of foundation models. We show that measuring uncertainty in natural language is challenging because of "semantic equivalence" -- different sentences can mean the same thing. To overcome these challenges we introduce semantic entropy -- an entropy which incorporates linguistic invariances created by shared meanings. Our method is unsupervised, uses only a single model, and requires no modifications to off-the-shelf language models. In comprehensive ablation studies we show that the semantic entropy is more predictive of model accuracy on question answering data sets than comparable baselines.

https://arxiv.org/pdf/2302.09664.pdf

 

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