TEMPORAL PROMPTING FOR LARGE LANGUAGE MODELS-BASED ON ENTITY LINKING
A methodical investigation of temporal prompting techniques for LLM-based EL is presented, and it is demonstrated that explicit temporal prompting can reduce drift mistakes by up to 40% using a dataset of temporally-sensitive mentions linked with Wikidata snapshots.
T. Thi, Thanh Thao
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