Prompt-tuning has become an increasingly popular parameter-efficient method for adapting large pretrained language models to downstream tasks. However, both discrete prompting and continuous prompting assume fixed prompts for all data samples within a task, neglecting the fact that inputs vary greatly in some tasks such as open-domain dialogue generation. In this paper, we present a novel, instance-specific prompt-tuning algorithm for dialogue generation. Specifically, we generate prompts based on instance-level control code, rather than the conversation history, to explore their impact on controlled dialogue generation. Experiments on popular open-domain dialogue datasets, evaluated on both automated metrics and human evaluation, demonstrate that our method is superior to prompting baselines and comparable to fine-tuning with only 5%-6% of total parameters.
@article{arxiv.2307.05228,
title = {Attribute Controlled Dialogue Prompting},
author = {Runcheng Liu and Ahmad Rashid and Ivan Kobyzev and Mehdi Rezagholizadeh and Pascal Poupart},
journal= {arXiv preprint arXiv:2307.05228},
year = {2023}
}