![]() ![]() If you're a guest, just Login (or Register, if you're not part of our community, it just takes 20 seconds) and the Download link will appearĭownload link not appearing? Don't panic: watch this simple video tutorial about how to install Blek or ask help to our community. You'll reach a page which will redirect you to our forum within a few seconds (if that doesn't happen, press the "Proceed" button at the top of that page).Click on one of the green "Download" buttons above. ![]() Significant performance gains over previous state-of-the-art methods.You can download Blek in three simple steps: Experimental results show that GDFO can achieve In this paper, we introduce gradientĭescent into black-box tuning scenario through knowledge distillation.įurthermore, we propose a novel method GDFO, which integrates gradient descentĪnd derivative-free optimization to optimize task-specific continuous prompts However, these gradient-free methods still exhibit a significant gapĬompared to gradient-based methods. (DFO), instead of gradient descent, for training task-specific continuous To solve the issue,īlack-box tuning has been proposed, which utilizes derivative-free optimization Gradients of PLMs are unavailable in this scenario. Furthermore, PLMs may not be open-sourced due to commercialĬonsiderations and potential risks of misuse, such as GPT-3. However, the cost of running these PLMs may be Download a PDF of the paper titled When Gradient Descent Meets Derivative-Free Optimization: A Match Made in Black-Box Scenario, by Chengcheng Han and 7 other authors Download PDF Abstract: Large pre-trained language models (PLMs) have garnered significant attentionįor their versatility and potential for solving a wide spectrum of natural ![]()
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