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cs.CL2024
Smaller Language Models Are Better Instruction Evolvers
Tingfeng Hui, Lulu Zhao, Guanting Dong +3
Instruction tuning has been widely used to unleash the complete potential of large language models. Notably, complex and diverse instructions are of significant importance as they…
cs.CL2023★ 2 cited
BitCoin: Bidirectional Tagging and Supervised Contrastive Learning based Joint Relational Triple Extraction Framework
Luyao He, Zhongbao Zhang, Sen Su +1
Relation triple extraction (RTE) is an essential task in information extraction and knowledge graph construction. Despite recent advancements, existing methods still exhibit certai…
cs.CL2023★ 1 cited
Quantifying and Analyzing Entity-level Memorization in Large Language Models
Zhenhong Zhou, Jiuyang Xiang, Chaomeng Chen +1
Large language models (LLMs) have been proven capable of memorizing their training data, which can be extracted through specifically designed prompts. As the scale of datasets cont…