
The author introduces DQ-LoRe, a framework leveraging Dual Queries and Low-rank approximation Re-ranking to enhance in-context learning. By selecting exemplars based on CoT and employing PCA for dimensionality reduction, DQ-LoRe outperforms existing methods in multi-step reasoning tasks.


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DQ-LoRe-Dual-Queries-with-Low-Rank-Approximation-Re-Ranking-for-In-Context-Learning


DQ-LoRe: Dual Queries with Low Rank Approximation Re-Ranking for In-Context Learning


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自動選択のためのDual QueriesとLow-rank近似再ランキングを活用するフレームワーク、DQ-LoReは、GPT-4の例示物の自動選択において優れたパフォーマンスを発揮します。


dq-lore-dual-queries-with-low-rank-approximation-re-ranking-for-in-context-learning-at-iclr-2024


DQ-LoRe: Dual Queries with Low Rank Approximation Re-Ranking for In-Context Learning at ICLR 2024



DQ-LoRe significantly enhances exemplar selection for in-context learning, outperforming existing methods.
