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cs.CL2024
Cross-Architecture Transfer Learning for Linear-Cost Inference Transformers
Sehyun Choi
Recently, multiple architectures has been proposed to improve the efficiency of the Transformer Language Models through changing the design of the self-attention block to have a li…
cs.CL2024
AbsInstruct: Eliciting Abstraction Ability from LLMs through Explanation Tuning with Plausibility Estimation
Zhaowei Wang, Wei Fan, Qing Zong +7
Abstraction ability is crucial in human intelligence, which can also benefit various tasks in NLP study. Existing work shows that LLMs are deficient in abstract ability, and how to…