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- W4385571455 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." @default.
- W4385571455 created "2023-08-05" @default.
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- W4385571455 date "2023-01-01" @default.
- W4385571455 modified "2023-10-14" @default.
- W4385571455 title "Attribute Controlled Dialogue Prompting" @default.
- W4385571455 doi "https://doi.org/10.18653/v1/2023.findings-acl.150" @default.
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