Curriculum Demonstration Selection for In-Context Learning

• URL: http://arxiv.org/abs/2411.18126v2
• Date: Sun, 15 Dec 2024 10:14:03 GMT
• Title: Curriculum Demonstration Selection for In-Context Learning
• Authors: Duc Anh Vu, Nguyen Tran Cong Duy, Xiaobao Wu, Hoang Minh Nhat, Du Mingzhe, Nguyen Thanh Thong, Anh Tuan Luu,
• Abstract summary: Large Language Models (LLMs) have shown strong in-context learning abilities with a few demonstrations. We propose Curriculum Demonstration Selection (CDS), a novel demonstration selection method for ICL. Instead of merely using similarity, CDS additionally partitions samples by their complexity measurements.
• Score: 19.951629335423466
• License:
• Abstract: Large Language Models (LLMs) have shown strong in-context learning (ICL) abilities with a few demonstrations. However, one critical challenge is how to select demonstrations to elicit the full potential of LLMs. In this paper, we propose Curriculum Demonstration Selection (CDS), a novel demonstration selection method for ICL. Instead of merely using similarity, CDS additionally partitions samples by their complexity measurements. Following curriculum learning, CDS then selects demonstrations from easy to difficult. Thus the selected demonstrations cover a wide range of difficulty levels, enabling LLMs to learn from varied complexities within the training set. Experiments demonstrate that our CDS consistently outperforms baseline methods, achieving notable improvements across nine LLMs on three benchmarks. Moreover, CDS proves especially effective in enhancing LLM performance in solving challenging problems.

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