论文标题
提示对话：如何控制对话模型？
Prompting for a conversation: How to control a dialog model?
论文作者
论文摘要
对话建模面临艰难的权衡。对模型进行了大量文本培训，但是他们的响应需要仅限于所需的范围和对话框的样式。由于用于实现前者包含与后者不兼容的语言的数据集在较小的策划数据集上进行了微调。但是，微调过程剥夺了他们产生各种反应的能力，最终将他们降低到乏味的对话伙伴。在本文中，我们调查提示是否可以减轻上述权衡。具体来说，我们试验了在查询上调节提示，而不是训练所有查询的单个提示。通过遵循直觉，即冻结预训练的语言模型将保留其表现力，我们发现与微调相比，提示可以达到更高的BLEU得分，并显着提高了响应的多样性和新颖性。
Dialog modelling faces a difficult trade-off. Models are trained on a large amount of text, yet their responses need to be limited to a desired scope and style of a dialog agent. Because the datasets used to achieve the former contain language that is not compatible with the latter, pre-trained dialog models are fine-tuned on smaller curated datasets. However, the fine-tuning process robs them of the ability to produce diverse responses, eventually reducing them to dull conversation partners. In this paper we investigate if prompting can mitigate the above trade-off. Specifically, we experiment with conditioning the prompt on the query, rather than training a single prompt for all queries. By following the intuition that freezing the pre-trained language model will conserve its expressivity, we find that compared to fine-tuning, prompting can achieve a higher BLEU score and substantially improve the diversity and novelty of the responses.
