most citedMAPO: Boosting Large Language Model Performance with Model-Adaptive Prompt Optimization

2 citations · 2 across the 3 of their papers we have counts for

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cs.CL2024★ 2 cited

MAPO: Boosting Large Language Model Performance with Model-Adaptive Prompt Optimization

Yuyan Chen, Zhihao Wen, Ge Fan +6

Prompt engineering, as an efficient and effective way to leverage Large Language Models (LLM), has drawn a lot of attention from the research community. The existing research prima…

cs.CL2024

Can Pre-trained Language Models Understand Chinese Humor?

Yuyan Chen, Zhixu Li, Jiaqing Liang +3

Humor understanding is an important and challenging research in natural language processing. As the popularity of pre-trained language models (PLMs), some recent work makes prelimi…

cs.CL2022

Tackling Math Word Problems with Fine-to-Coarse Abstracting and Reasoning

Ailisi Li, Xueyao Jiang, Bang Liu +2

Math Word Problems (MWP) is an important task that requires the ability of understanding and reasoning over mathematical text. Existing approaches mostly formalize it as a generati…