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

TL;DR

This paper explores how prompt conditioning can better control dialog models, maintaining diversity and style without fine-tuning, by leveraging pre-trained language models' expressivity.

Contribution

It introduces a prompt conditioning approach that improves response diversity and style control in dialog models compared to traditional fine-tuning methods.

Findings

Prompt conditioning outperforms fine-tuning in response diversity.

Higher BLEU scores achieved with prompt conditioning.

Enhanced novelty and control in dialog responses.

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.