You Only Edit Once: Incentivizing In-Context Capability of LLMs via Local Demonstration Refinement
In-context learning (ICL) is crucial for boosting the inference performance of large language models (LLMs). However, the effectiveness of ICL in LLMs is greatly influenced by the choice of demonstration sets. Exhaustive searches over these sets are combinatorial, and existing selectors often rely on relevance or likel...
Jia-Rong Wen, Qi Wang, Yun Qu et al.
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