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pro vyhledávání: '"Cai, Yujun"'
Instruction-tuned large language models (LLMs), such as ChatGPT, have led to promising zero-shot performance in discriminative natural language understanding (NLU) tasks. This involves querying the LLM using a prompt containing the question, and the
Externí odkaz:
http://arxiv.org/abs/2310.13206
Autor:
Wang, Yiwei, Hooi, Bryan, Wang, Fei, Cai, Yujun, Liang, Yuxuan, Zhou, Wenxuan, Tang, Jing, Duan, Manjuan, Chen, Muhao
Relation extraction (RE) aims to extract the relations between entity names from the textual context. In principle, textual context determines the ground-truth relation and the RE models should be able to correctly identify the relations reflected by
Externí odkaz:
http://arxiv.org/abs/2305.13551
Entity types and textual context are essential properties for sentence-level relation extraction (RE). Existing work only encodes these properties within individual instances, which limits the performance of RE given the insufficient features in a si
Externí odkaz:
http://arxiv.org/abs/2205.03786
Autor:
Wang, Yiwei, Chen, Muhao, Zhou, Wenxuan, Cai, Yujun, Liang, Yuxuan, Liu, Dayiheng, Yang, Baosong, Liu, Juncheng, Hooi, Bryan
Recent literature focuses on utilizing the entity information in the sentence-level relation extraction (RE), but this risks leaking superficial and spurious clues of relations. As a result, RE still suffers from unintended entity bias, i.e., the spu
Externí odkaz:
http://arxiv.org/abs/2205.03784