Projection-Free Evolution Strategies for Continuous Prompt Search
Yu Cai, Canxi Huang, and Xiaoyu He

TL;DR
This paper introduces a projection-free evolutionary strategy for continuous prompt search in NLP, directly optimizing in the full prompt space, leading to improved performance over traditional projection-based methods.
Contribution
It proposes a novel projection-free prompt search method that directly optimizes in the full prompt space and includes a confidence-based regularization for better few-shot generalization.
Findings
Outperforms existing baselines on GLUE tasks
Direct optimization in full prompt space is effective
Projection mechanisms may not capture essential prompt structure
Abstract
Continuous prompt search offers a computationally efficient alternative to conventional parameter tuning in natural language processing tasks. Nevertheless, its practical effectiveness can be significantly hindered by the black-box nature and the inherent high-dimensionality of the objective landscapes. Existing methods typically mitigate these challenges by restricting the search to a randomly projected low-dimensional subspace. However, the effectiveness and underlying motivation of the projection mechanism remain ambiguous. In this paper, we first empirically demonstrate that despite the prompt space possessing a low-dimensional structure, random projections fail to adequately capture this essential structure. Motivated by this finding, we propose a projection-free prompt search method based on evolutionary strategies. By directly optimizing in the full prompt space with an…
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Taxonomy
TopicsTopic Modeling · Multimodal Machine Learning Applications · Natural Language Processing Techniques
