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Zero-shot Entity and Tweet Characterization with Designed Conditional Prompts and Contexts
In this work, we approach the problem of characterizing Named Entities and Tweets as an open-ended text classification and open-ended fact probing problem. We evaluate the zero-shot language model capabilities of Generative Pretrained Transformer 2 (GPT-2) to characterize Entities and Tweets subjectively with human psychology-inspired and logical conditional prefixes and contexts.
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