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cs.CL2024
SCOI: Syntax-augmented Coverage-based In-context Example Selection for Machine Translation
Chenming Tang, Zhixiang Wang, Yunfang Wu
In-context learning (ICL) greatly improves the performance of large language models (LLMs) on various down-stream tasks, where the improvement highly depends on the quality of demo…
cs.CL2024★ 1 cited
Large Language Models Might Not Care What You Are Saying: Prompt Format Beats Descriptions
Chenming Tang, Zhixiang Wang, Hao Sun +1
With the help of in-context learning (ICL), large language models (LLMs) have achieved impressive performance across various tasks. However, the function of descriptive instruction…
cs.CL2024
Going Beyond Word Matching: Syntax Improves In-context Example Selection for Machine Translation
Chenming Tang, Zhixiang Wang, Yunfang Wu
In-context learning (ICL) is the trending prompting strategy in the era of large language models (LLMs), where a few examples are demonstrated to evoke LLMs' power for a given task…