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• W4386804501 abstract "Existing knowledge-grounded conversation systems generate responses typically in a retrieve-then-generate manner. They require a large knowledge base and a strong knowledge retrieval component, which is time- and resource-consuming. In this paper, we address the challenge by leveraging the inherent knowledge encoded in the pre-trained language models (PLMs). We propose Knowledgeable Prefix Tuning (KnowPrefix-Tuning), a two-stage tuning framework, bypassing the retrieval process in a knowledge-grounded conversation system by injecting prior knowledge into the lightweight knowledge prefix. The knowledge prefix is a sequence of continuous knowledge-specific vectors that can be learned during training. In addition, we propose a novel interactive re-parameterization mechanism that allows the prefix to interact fully with the PLM during the optimization of response generation. Experimental results demonstrate that KnowPrefix-Tuning outperforms fine-tuning and other lightweight tuning approaches, and performs comparably with strong retrieval-based baselines while being $$3times $$ faster during inference (The code is available at https://github.com/fantast4ever/KnowPrefix-Tuning .)" @default.
• W4386804501 created "2023-09-17" @default.
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• W4386804501 date "2023-01-01" @default.
• W4386804501 modified "2023-09-27" @default.
• W4386804501 title "KnowPrefix-Tuning: A Two-Stage Prefix-Tuning Framework for Knowledge-Grounded Dialogue Generation" @default.
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• W4386804501 doi "https://doi.org/10.1007/978-3-031-43415-0_31" @default.
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