不用去做大模型的训练,只需要训练一个小的sequence to sequence模型 黑盒Prompt优化,让模型更好地对齐人类的意图 不同的Prompt带来的效果不一样,论文的核心: 在输入一个Prompt A之后,是否有方法可以自动优化Prompt A,得到PromptA′,然后将PromptA′喂给大模型,使得大模型产生的效果更好 背景介绍 动机 随着LLM模型...
【论文阅读】Black-Box Prompt Optimization: Aligning Large Language Models without Model Training 陈昊 金融业 算法工程师125 人赞同了该文章 (最近看到一篇文章,佩服得五体投地,单论做文章的本事,确实值得学习,一点发散,居然洋洋洒洒上万字) (文章链接 arxiv.org/pdf/2311.0415) (代码链接 github.com/thu...
For this, we devise Coordinator, which reparameterizes the prompt as an autoencoder to handle the input-dependent prompt with tiny parameters. New Algorithm for Black-Box Optimization: We propose a new zeroth-order optimization algorithm, SPSA-GC, that gives look-ahead corrections to the SPSA's...
However, the performance of derivative-free optimization techniques deteriorates quickly for large-scale problems. As more classes need to be forgotten, the "latent context" used to optimize the input prompts grows to unmanageable sizes. To address this issue, the research team came up with a new...
Black-box optimization algorithms have been widely used in various machine learning problems, including reinforcement learning and prompt fine-tuning. However, directly optimizing the training loss value, as commonly done in existing black-box optimization methods, could lead to suboptimal model quality ...
Text-to-Image Optimization Prompt Inversion Citation If you use this code in your research, please kindly cite the following papers: @misc{liu2024language, title={Language Models as Black-Box Optimizers for Vision-Language Models}, author={Shihong Liu and Zhiqiu Lin and Samuel Yu and Ryan Lee...
Given that information on the model is unavailable, we optimize the input prompt to decrease the accuracy of specified classes through derivative-free optimization. To avoid difficult high-dimensional optimization while ensuring high forgetting performance, we propose Latent Context Sharing, which ...
Robust Prompt Optimization for Defending Language Models Against Jailbreaking Attacks Andy Zhou, Bo Li, Haohan Wang 2024 Defending Against Alignment-Breaking Attacks via Robustly Aligned LLM Bochuan Cao, Yu Cao, Lu Lin, Jinghui Chen 2023 DeepInception: Hypnotize Larg...
当我们只能访问PTM inference(推理)API时,我们可以优化 task-specific(特定于任务的) continuous prompt吗?由于梯度不可用,我们只能调用derivative-free optimization无导数优化 (DFO)3(Kolda等人,2003年;Conn等人,2009年;Rios&Sahinidis,2013年)。DFO涉及一种不依赖于梯度的优化算法,而只依赖于sampled solutions(采样解...
相比之前ICML版里的方法,BBTv2完全摆脱了对梯度(预训练prompt)的依赖,能在完全无梯度情况下优化更少的参数取得和模型微调可比甚至更好的准确率。 BBTv2: Pure Black-Box Optimization Can Be Comparable to Gradient Descent for Few-Shot Learning 链接