Prompting for a conversation: How to control a dialog model?

• URL: http://arxiv.org/abs/2209.11068v1
• Date: Thu, 22 Sep 2022 14:59:55 GMT
• Title: Prompting for a conversation: How to control a dialog model?
• Authors: Josef Valvoda, Yimai Fang, David Vandyke
• Abstract summary: Dialog 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. In this paper we investigate if prompting can mitigate the above trade-off.
• Score: 9.268682116424518
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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