CPT: Consistent Proxy Tuning for Black-box Optimization
Keywords: black-box models, large language models, vision-language models, black-box tuning
TL;DR: In this paper we propose a novel method of tuning black-box models called Consistent Proxy Tuning (CPT), which is model-agnostic and applicable to many large pretrain models, e.g. LLMs, VLMs, etc.
Abstract: Black-box tuning has attracted recent attention due to that the structure or inner parameters of advanced proprietary models are not accessible. Recently, Proxy-tuning provides a test-time output adjustment for tuning black-box language models.It applies the difference of the output logits before and after tuning a smaller white-box "proxy" model to improve the black-box model. However, this technique serves only as a decoding-time algorithm, leading to an inconsistency between training and testing which potentially limits overall performance. To address this problem, we introduce Consistent Proxy Tuning (CPT), a simple yet effective black-box tuning method. Different from Proxy-tuning, CPT additionally exploits the frozen large black-box model and another frozen small white-box model, ensuring consistency between training-stage optimization objective and test-time proxies. This consistency benefits Proxy-tuning and enhances model performance. Note that our method focuses solely on logit-level computation, which makes it model-agnostic and applicable to any task involving logit classification. Extensive experimental results demonstrate the superiority of our CPT in both black-box tuning of Large-Language Models (LLMs) and Vision-Language Models (VLMs) across various datasets.
Primary Area: optimization
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Submission Number: 3016
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