2 citations · 4 across the 4 of their papers we have counts for
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cs.CL2024★ 2 cited
Evolving Knowledge Distillation with Large Language Models and Active Learning
Chengyuan Liu, Yangyang Kang, Fubang Zhao +4
Large language models (LLMs) have demonstrated remarkable capabilities across various NLP tasks. However, their computational costs are prohibitively high. To address this issue, p…
cs.CL2024★ 1 cited
Recurrent Alignment with Hard Attention for Hierarchical Text Rating
Chenxi Lin, Jiayu Ren, Guoxiu He +3
While large language models (LLMs) excel at understanding and generating plain text, they are not tailored to handle hierarchical text structures or directly predict task-specific…
cs.CL2020
Read Beyond the Lines: Understanding the Implied Textual Meaning via a Skim and Intensive Reading Model
Guoxiu He, Zhe Gao, Zhuoren Jiang +4
The nonliteral interpretation of a text is hard to be understood by machine models due to its high context-sensitivity and heavy usage of figurative language. In this study, inspir…