2024/02/21 by Haoyu Liu, Liu, Haoyu, Jianfeng Liu +13 · 1 citation
Computer Science · Medicine · Neuroscience · #Brain Tumor Detection and Classification #COVID-19 diagnosis using AI #Computation and Language (cs.CL) #Domain Adaptation and Few-Shot Learning #FOS: Computer and information sciences
paper · pdf · doi:10.48550/arxiv.2402.13874
openalex publication_date 2024/02/21 · openalex created_date 2024/02/23 · openalex updated_date 2026/07/28
The remarkable capability of large language models (LLMs) for in-context learning (ICL) needs to be activated by demonstration examples. Prior work has extensively explored the selection of examples for ICL, predominantly following the "select then organize" paradigm, such approaches often neglect the internal relationships between examples and exist an inconsistency between the training and inference. In this paper, we formulate the problem as a Sequential Selection problem and introduce Se2, a sequential-aware method that leverages the LLM's feedback on varying context, aiding in capturing inter-relationships and sequential information among examples, significantly enriching the contextuality and relevance of ICL prompts. Meanwhile, we utilize beam search to seek and construct example sequences, enhancing both quality and diversity. Extensive experiments across 23 NLP tasks from 8 distinct categories illustrate that Se2 markedly surpasses competitive baselines and achieves 42% relative improvement over random selection. Further in-depth analysis shows the effectiveness of proposed strategies, highlighting Se2's exceptional stability and adaptability across various scenarios. Code available at https://github.com/microsoft/LMOps.