@inproceedings{liu-etal-2023-attribute,
title = "Attribute Controlled Dialogue Prompting",
author = "Liu, Runcheng and
Rashid, Ahmad and
Kobyzev, Ivan and
Rezagholizadeh, Mehdi and
Poupart, Pascal",
editor = "Rogers, Anna and
Boyd-Graber, Jordan and
Okazaki, Naoaki",
booktitle = "Findings of the Association for Computational Linguistics: ACL 2023",
month = jul,
year = "2023",
address = "Toronto, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.findings-acl.150",
doi = "10.18653/v1/2023.findings-acl.150",
pages = "2380--2389",
abstract = "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.",
}
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<abstract>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.</abstract>
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%0 Conference Proceedings
%T Attribute Controlled Dialogue Prompting
%A Liu, Runcheng
%A Rashid, Ahmad
%A Kobyzev, Ivan
%A Rezagholizadeh, Mehdi
%A Poupart, Pascal
%Y Rogers, Anna
%Y Boyd-Graber, Jordan
%Y Okazaki, Naoaki
%S Findings of the Association for Computational Linguistics: ACL 2023
%D 2023
%8 July
%I Association for Computational Linguistics
%C Toronto, Canada
%F liu-etal-2023-attribute
%X 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.
%R 10.18653/v1/2023.findings-acl.150
%U https://aclanthology.org/2023.findings-acl.150
%U https://doi.org/10.18653/v1/2023.findings-acl.150
%P 2380-2389
Markdown (Informal)
[Attribute Controlled Dialogue Prompting](https://aclanthology.org/2023.findings-acl.150) (Liu et al., Findings 2023)
ACL
- Runcheng Liu, Ahmad Rashid, Ivan Kobyzev, Mehdi Rezagholizadeh, and Pascal Poupart. 2023. Attribute Controlled Dialogue Prompting. In Findings of the Association for Computational Linguistics: ACL 2023, pages 2380–2389, Toronto, Canada. Association for Computational Linguistics.