Tag
This paper proposes using state space models to efficiently select demonstrations for long-context language model prompts, reducing computational cost and improving performance.
SALA is a Semantic-Aware Logical Alignment framework that improves demonstration selection for complex reasoning in in-context learning by automatically learning task-specific reasoning operations and using dynamic time warping for flexible alignment, outperforming existing methods.
This paper introduces MAG, a manifold-guided framework for semi-supervised multi-modal in-context demonstration selection, leveraging unlabeled data to improve few-shot ICL for MLLMs. Experiments on eight benchmarks show consistent gains in label-scarce regimes.
This paper investigates many-shot chain-of-thought in-context learning for reasoning tasks, revealing that standard scaling rules do not transfer and proposing Curvilinear Demonstration Selection (CDS) for improved ordering, achieving up to 5.42 percentage-point gain.