在《Dynamic Prompt Learning: Addressing Cross-Attention Leakage for Text-Based Image Editing》这篇论文中,作者通过一系列实验展示了 Dynamic Prompt Learning 在基于文本的图像编辑任务中的效果。实验结果表明,使用 Dynamic Prompt Learning 可以显著提高生成图像与文本提示之间的一致性,同时减少 cross-attention leakage...
Source:ICLR2023Dynamic Prompt Learning via Policy Gradient for Semi-structured Mathematical Reasoning Code:github.com/lupantech/Pr 背景 数学推理是人类智能的一项核心能力,但对于机器来说,抽象思维和逻辑推理仍然是一个很大的挑战。大规模预训练语言模型,如 GPT-3 和 GPT-4,在文本形式的数学推理(如数学应用题...
《Dynamic Prompt Learning: Addressing Cross-Attention Leakage for Text-Based Image Editing》(NeurIPS 2023) GitHub: github.com/wangkai930418/DPL《GNNEvaluator: Evaluating GNN Performance On Unseen Graphs Without Labels》(NeurIPS 2023) GitHub: github.com/Amanda-Zheng/GNNEvaluator...
In addition, DPaRL jointly learns dynamic prompt generation and discriminative representation at each training stage whereas prior PCL methods only refine the prompt learning throughout the process. Our experimental results demonstrate the superiority of our approach, surpassing state-of-the-art methods ...
Data and code for our ICLR 2023 PaperDynamic Prompt Learning via Policy Gradient for Semi-structured Mathematical Reasoning. For more details, please refer to the project page with dataset exploration and visualization tools:https://promptpg.github.io. ...
ignoring the diverse traditional or learning-based codecs in the practical application, e.g., HEVC, VVC, HIFIC, etc. In this work, we propose the first universal CSR framework, dubbed UCIP, with dynamic prompt learning, intending to jointly support the CSR distortions of any compression codecs...
“gist token” activations during finetuning. However, this simple idea is ineffective in compressing API documentation, resulting in low accuracy compared to the baseline using an uncompressed prompt. In this work, we introduce two major improvements. First, we specialize gist tokens for different ...
Besides refining the original prompts for image generation, we further employ an online reinforcement learning strategy to explore the weights and injection time steps of each word, leading to the dynamic fine-control prompts. The reward function during training encourages the model to consider ...
this method ensures that only the most pertinent examples are included in the prompt, thereby optimizing its size and relevance. This dynamic technique not only maintains the efficiency and effectiveness of few-shot learning but also enhances the model’s ability to generate accurate and contex...
introduce a novel learning approach that dynamically selects the optimal prompt strategy, LLM model, and embedding model per query at run-time. This dynamic adaptation maximizes the efficacy of LLMs across languages, outperforming best static and random strate...