Representative Demonstration Selection for In-Context Learning with Two-Stage Determinantal Point Process
文献类型:会议论文
作者 | Zhao Yang2,3; Yuanzhe Zhang2,3![]() ![]() ![]() ![]() |
出版日期 | 2023 |
会议日期 | 2023-12 |
会议地点 | Singapore |
国家 | Singapore |
英文摘要 | Although In-Context Learning has proven effective across a broad array of tasks, its efficiency is noticeably influenced by the selection of demonstrations. Existing methods tend to select different demonstrations for each test instance, which is time-consuming and poses limitations in practical scenarios. Therefore, this study aims to address the challenge of selecting a representative subset of in-context demonstrations that can effectively prompt different test instances in a specific task. We propose that this representative subset should be of high quality and diversity. Our empirical analyses confirm that demonstrations that meet these criteria can indeed bolster model performance. To satisfy these criteria, this paper further introduces a two-stage Determinantal Point Process (DPP) method designed to incorporate both quality and diversity in the process of demonstration selection, thereby obtaining representative in-context demonstrations. Through comprehensive experimentation, we have confirmed the efficacy of our proposed method, paving the way for more practical and effective In-Context Learning. |
源URL | [http://ir.ia.ac.cn/handle/173211/56724] ![]() |
专题 | 复杂系统认知与决策实验室 |
通讯作者 | Kang Liu |
作者单位 | 1.Meituan, Beijing, China 2.School of Artificial Intelligence, University of Chinese Academy of Sciences, China 3.The Laboratory of Cognition and Decision Intelligence for Complex Systems, Institute of Automation, Chinese Academy of Sciences, China 4.Shanghai Artificial Intelligence Laboratory, China 5.Harbin Institute of Technology, Weihai, China |
推荐引用方式 GB/T 7714 | Zhao Yang,Yuanzhe Zhang,Dianbo Sui,et al. Representative Demonstration Selection for In-Context Learning with Two-Stage Determinantal Point Process[C]. 见:. Singapore. 2023-12. |
入库方式: OAI收割
来源:自动化研究所
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