自然语言理解:在问答系统领域,Prompt-based Language Models可以根据问题中的关键词或短语,从大量的文档中快速找到相关的信息,并生成简洁明了的答案。 机器翻译:在跨语言信息检索领域,Prompt-based Language Models可以根据给定的原文和目标语言,快速生成准确的翻译结果。 情感分析:在情感分析领域,Prompt-based Language Mo...
另⼀个灵感来⾃预训练语⾔模型的 Masked Language Model/MLM 任务：在 BERT 的训练中，有 15% 的输⼊词被选中，其中的绝⼤部分⼜被替换为 [MASK] 标签或者随 机的其他词，并在最终的 hidden states 中对被遮盖的词进⾏预测，通过还原遮盖词让模型学习 单词级别的上下⽂信息。将这两个灵感融合，...
Specifically, the prompt is designed to provide the extra knowledge for enhancing the pre-trained model. Data augmentation and model ensemble are adopted for obtaining better results. Extensive experiments are performed, which shows the effectiveness of the proposed method. On the final submission, ...
from keras.layers import Lambda, Dense # 加载预训练模型和分词器config_path = './model/bert_config.json' checkpoint_path = './model/bert_model.ckpt' dict_path = './model/vocab.txt' maxlen = 50 # 这里with_mlm 打开预测单字模式 model = build_transformer_model( config_path=config_path, ...
In its application, when a model receives a prompt, it will attempt to produce the next series of words that are most likely to follow the previously given text. For example, if we insert this text"What is the first thing that comes to your mind when I say <pro...
label_ids=np.array([tokenizer.encode(l)[0][1:-1]forl in labels])y_pred=model.predict([token_ids,segment_ids])[:,mask_idxs]y_pred=y_pred[:,0,label_ids[:,0]]y_pred[:,1,label_ids[:,1]]y_pred=y_pred.argmax(axis=1)labels[y_pred[0]] ...
The quality of the output generated by a prompt-based model is highly dependent on the quality of the prompt. A well-crafted prompt can help the model generate more accurate and relevant outputs, while a poorly crafted prompt can lead to incoherent or irrelevant outputs. The art of writing ...
(2)we define the textual concatenation of different input-output pairs as Prompt_k= {Shot0, ··· , Shotk}, shot_i 是根据输入-输出对(X,Y)转为text, 使用特定任务的模板。因此,给出一个Prompt 并且一个input X, the model generates the output Y by sampling from ...
Methods AddRemove Contrastive Learning
Specifically, we first utilize GPT4 to reformulate manual templates to generate tone-strong and normal templates, and the former are injected into the model as a backdoor trigger in the pre-training phase. Then, we not only directly employ the above templates in the downstream task, but also...