Efficient and Effective In-context Demonstration Selection with Coreset
This addresses the efficiency-effectiveness trade-off in demonstration selection for LVLMs, though it appears incremental as it builds on prior coreset and retrieval methods.
The paper tackles the NP-hard problem of selecting effective demonstrations for in-context learning in Large Visual Language Models, proposing a Coreset-based Dual Retrieval (CoDR) framework that significantly improves performance over existing strategies.
In-context learning (ICL) has emerged as a powerful paradigm for Large Visual Language Models (LVLMs), enabling them to leverage a few examples directly from input contexts. However, the effectiveness of this approach is heavily reliant on the selection of demonstrations, a process that is NP-hard. Traditional strategies, including random, similarity-based sampling and infoscore-based sampling, often lead to inefficiencies or suboptimal performance, struggling to balance both efficiency and effectiveness in demonstration selection. In this paper, we propose a novel demonstration selection framework named Coreset-based Dual Retrieval (CoDR). We show that samples within a diverse subset achieve a higher expected mutual information. To implement this, we introduce a cluster-pruning method to construct a diverse coreset that aligns more effectively with the query while maintaining diversity. Additionally, we develop a dual retrieval mechanism that enhances the selection process by achieving global demonstration selection while preserving efficiency. Experimental results demonstrate that our method significantly improves the ICL performance compared to the existing strategies, providing a robust solution for effective and efficient demonstration selection.