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arXiv · 2410.07523

DemoShapley: Valuation of Demonstrations for In-Context Learning

Shan Xie·Man Luo·Chadly Daniel Stern·Mengnan Du·Lu Cheng
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Abstract

Large language models (LLMs) using in-context learning (ICL) excel in many tasks without task-specific fine-tuning. However, demonstration selection and ordering greatly impact ICL effectiveness. Focus on this issue, we propose DemoShapley, a Shapley-value based method that evaluates each demonstration's contribution by measuring its marginal effect across different prompt permutations. To further account for ICL's limited context windows and frequent low-shot settings, we introduce Beta-DemoShapley, a weighted extension that emphasizes the influence of smaller prompt sizes. Experiments on multiple benchmarks show that DemoShapley consistently outperforms existing influence-based selection strategies, while Beta-DemoShapley further improves performance in low-shot scenarios. Both methods also detect mislabeled data, enhance generalization to out-of-distribution tasks, and reduce demographic bias. Together, they provide a unified and robust framework for demonstration valuation in ICL.

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Shan Xie, Man Luo, Chadly Daniel Stern, Mengnan Du, Lu Cheng. 2024-10-10. DemoShapley: Valuation of Demonstrations for In-Context Learning. https://arxiv.org/abs/2410.07523

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