![]() Intuitive controls: All you have to do to play this game is make a little swipe motion with your finger in the direction you want the line you're creating to go. ![]() This single move creates a little line that hops across the screen in the direction you set with your swipe, and destroys all bubbles it meets along the way. Significant performance gains over previous state-of-the-art methods.Blek is an intriguing and addictive game that puts your spatial reasoning skills to the test by challenging you to pop all bubbles on the screen with one move. 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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