Applying language models to natural language processing tasks typically relies on the representations in the final model layer, as intermediate hidden layer representations are presumed to be less informative. In this work, we argue that due to the gradual improvement across model layers, additional information can be gleaned from the contrast between higher and lower layers during inference. Specifically, in choosing between the probable next token predictions of a generative model, the predictions of lower layers can be used to highlight which candidates are best avoided. We propose a novel approach that utilizes the contrast between layers to improve text generation outputs, and show that it mitigates degenerative behaviors of the model in open-ended generation, significantly improving the quality of generated texts. Furthermore, our results indicate that contrasting between model layers at inference time can yield substantial benefits to certain aspects of general language model capabilities, more effectively extracting knowledge during inference from a given set of model parameters.

本文提出了一种新方法，利用模型层次之间的对比来改善文本生成输出，并显示它可以缓解开放性生成模型的退化行为，显着提高生成文本的质量。另外，我们的结果表明，在推理时对比模型层可以从给定的模型参数集中更有效地提取知识，从而为一定方面的通用语言模型能力带来实质性的益处。

糟糕建议的好处：模型层之间的自动对比解码