文本嵌入测验

当您参加测验时，就会出现答案和解释。

1. What does a text embedding supply for a language comparison task?
   • A required spoken audio recording
   • A verbatim dictionary entry for every coordinate
   • A numerical vector representation of text
   • Only a grammar-repair instruction
2. In a representation trained to capture synonym similarity, what relationship is desirable for happy and joyful?
   • A relatively high similarity under the chosen scoring method
   • A mandatory identical vector for every word
   • A guarantee they are synonyms in every possible context
   • A requirement that neither word can be encoded
3. Word2Vec 和 GloVe 等早期词嵌入的关键限制是什么？
   • 它们无法存储在计算机上
   • 他们只适用于图像
   • 他们不需要训练数据
   • 无论上下文如何，每个单词都有一个固定的向量，因此“银行”在任何地方都意味着相同的事物
4. 现代上下文嵌入如何改进静态词嵌入？
   • 他们从表示中删除所有数字
   • 他们根据单词周围的句子赋予单词不同的向量
   • 他们只嵌入每个单词的第一个字母
   • 他们使每个单词的向量相同
5. Which documented default similarity method does Sentence Transformers use for embedding comparison?
   • 余弦相似度
   • Alphabetical order
   • Document creation date
   • Character count alone
6. What role can compatible text vectors play in a dense-retrieval RAG pipeline?
   • Guaranteeing every generated claim is correct
   • Eliminating the source corpus
   • Ranking candidate passages before they are supplied as context
   • Training a new language model on every query
7. Which pooling strategy is the stated default in the original Sentence-BERT paper?
   • A mandatory maximum over every possible vocabulary item
   • Mean pooling of output token vectors
   • Concatenation of all corpus documents
   • Randomly selecting an unchanged input byte
8. Which Sentence-BERT design allows stored sentences to be encoded before a new comparison sentence arrives?
   • Mandatory joint processing of every possible pair
   • Rebuilding encoder weights for each candidate
   • Encoding only the final comparison score
   • Independent sentence encoding through its siamese architecture