7 citations · 17 across the 10 of their papers we have counts for
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cs.AI2024★ 3 cited
Prompt Optimization with EASE? Efficient Ordering-aware Automated Selection of Exemplars
Zhaoxuan Wu, Xiaoqiang Lin, Zhongxiang Dai +5
Large language models (LLMs) have shown impressive capabilities in real-world applications. The capability of in-context learning (ICL) allows us to adapt an LLM to downstream task…
cs.AI2024★ 1 cited
Localized Zeroth-Order Prompt Optimization
Wenyang Hu, Yao Shu, Zongmin Yu +5
The efficacy of large language models (LLMs) in understanding and generating natural language has aroused a wide interest in developing prompt-based methods to harness the power of…