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This paper presents a novel approach to zero-shot slot filling by utilizing a hybrid prompting scheme called Slot-Prefix prompting, which combines learnable tokens with slot descriptions to enhance the performance of pre-trained language models (PLMs). The method leverages attention relationship descriptors to characterize relationships between tokens, enabling better span localization for unseen slot types. Experimental results demonstrate that this approach outperforms existing state-of-the-art methods in
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