GRaSp: Automatic Example Optimization for In-Context Learning in Low-Data Tasks
Addresses the challenge of selecting effective demonstrations for in-context learning in domain-specific, low-data settings, where high-quality examples are scarce.
GRaSp improves in-context learning for low-data NER tasks by automatically selecting optimal examples, achieving 45.84% micro-F1 on FiNER-139, outperforming zero-shot and random few-shot baselines.
In-context learning enables large language models to adapt to new tasks, but their performance is highly sensitive to the selected examples. Finding effective demonstrations is particularly difficult in domain-specific, low-data settings where high-quality examples are scarce. We propose GRaSp, a three-stage framework for automatic in-context example optimization. By first generating a large synthetic candidate pool, then structuring it with clustering and dimensionality reduction, and finally using genetic algorithms to find the optimal in-context examples, the framework shows consistent improvements on the NER task. We also introduce a custom diversity-adaptive mutation mechanism, allowing it to transition from the initial broad inter-cluster exploration to focused intra-cluster refinement as the population converges. We evaluate GRaSp on financial named entity recognition (FiNER-139), comparing synthetic and human-annotated candidate pools across pool sizes of 500 and 5000. With non-synthetic data, GRaSp achieves 45.84% micro-F1, consistently outperforming both zero-shot and random few-shot baselines. Synthetic data matches the random baseline but does not exceed it, suggesting that distributional variety in the candidate pool is critical for generalization.