GRaSp optimizes in-context examples for LLMs via synthetic generation, clustering, dimensionality reduction, and genetic algorithms with diversity-adaptive mutation, reaching 45.84% micro-F1 on financial NER with real data and outperforming zero-shot and random few-shot baselines.
In: Findings of the Association for Computational Linguistics: ACL- IJCNLP 2021
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GRaSp: Automatic Example Optimization for In-Context Learning in Low-Data Tasks
GRaSp optimizes in-context examples for LLMs via synthetic generation, clustering, dimensionality reduction, and genetic algorithms with diversity-adaptive mutation, reaching 45.84% micro-F1 on financial NER with real data and outperforming zero-shot and random few-shot baselines.