CPT: Consistent Proxy Tuning for Black-box Optimization

• URL: http://arxiv.org/abs/2407.01155v1
• Date: Mon, 1 Jul 2024 10:23:14 GMT
• Title: CPT: Consistent Proxy Tuning for Black-box Optimization
• Authors: Yuanyang He, Zitong Huang, Xinxing Xu, Rick Siow Mong Goh, Salman Khan, Wangmeng Zuo, Yong Liu, Chun-Mei Feng,
• Abstract summary: Proxy-tuning provides a test-time output adjustment for tuning black-box language models. We introduce Consistent Proxy Tuning (CPT), a simple yet effective black-box tuning method. CPT 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.
• Score: 63.06335358432746
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Black-box tuning has attracted recent attention due to that the structure or inner parameters of advanced proprietary models are not accessible. 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. The code is available at https://github.com/chunmeifeng/CPT.

Related papers

• Connecting the Dots: Collaborative Fine-tuning for Black-Box Vision-Language Models [121.0693322732454]
  This paper proposes a textbfCraFT' approach for fine-tuning black-box vision-language models to downstream tasks. CraFT comprises two modules, a prompt generation module for learning text prompts and a prediction refinement module for enhancing output predictions in residual style. Experiments on few-shot classification over 15 datasets demonstrate the superiority of CraFT.
  arXiv  Detail & Related papers  (2024-02-06T14:53:19Z)
• Tuning Language Models by Proxy [110.49482736590907]
  We introduce proxy-tuning, a lightweight decoding-time algorithm that operates on top of black-box LMs to achieve the same end as direct tuning. Our method tunes a smaller LM, then applies the difference between the predictions of the small tuned and untuned LMs to shift the original predictions of the larger untuned model in the direction of tuning.
  arXiv  Detail & Related papers  (2024-01-16T18:49:55Z)
• Black-Box Tuning of Vision-Language Models with Effective Gradient Approximation [71.21346469382821]
  We introduce collaborative black-box tuning (CBBT) for both textual prompt optimization and output feature adaptation for black-box models. CBBT is extensively evaluated on eleven downstream benchmarks and achieves remarkable improvements compared to existing black-box VL adaptation methods.
  arXiv  Detail & Related papers  (2023-12-26T06:31:28Z)
• Enhancing Black-Box Few-Shot Text Classification with Prompt-Based Data Augmentation [42.05617728412819]
  We show how to optimize few-shot text classification without accessing the gradients of the large-scale language models. Our approach, dubbed BT-Classifier, significantly outperforms state-of-the-art black-box few-shot learners.
  arXiv  Detail & Related papers  (2023-05-23T07:54:34Z)
• Black-Box Tuning for Language-Model-as-a-Service [85.2210372920386]
  This work proposes the Black-Box Tuning to optimize PTMs through derivative-free algorithms. In particular, we invoke the CMA-ES to optimize the continuous prompt prepended to the input text by iteratively calling PTM inference APIs. Our experimental results demonstrate that, black-box tuning with RoBERTa on a few labeled samples not only significantly outperforms manual prompt and GPT-3's in-context learning, but also surpasses the gradient-based counterparts.
  arXiv  Detail & Related papers  (2022-01-10T18:17:05Z)
• Non-smooth Bayesian Optimization in Tuning Problems [5.768843113172494]
  Building surrogate models is one common approach when we attempt to learn unknown black-box functions. We propose a novel additive Gaussian process model called clustered Gaussian process (cGP), where the additive components are induced by clustering. In the examples we studied, the performance can be improved by as much as 90% among repetitive experiments.
  arXiv  Detail & Related papers  (2021-09-15T20:22:09Z)
• Stepwise Model Selection for Sequence Prediction via Deep Kernel Learning [100.83444258562263]
  We propose a novel Bayesian optimization (BO) algorithm to tackle the challenge of model selection in this setting. In order to solve the resulting multiple black-box function optimization problem jointly and efficiently, we exploit potential correlations among black-box functions. We are the first to formulate the problem of stepwise model selection (SMS) for sequence prediction, and to design and demonstrate an efficient joint-learning algorithm for this purpose.
  arXiv  Detail & Related papers  (2020-01-12T09:42:19Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.