텍스트 임베딩 퀴즈

퀴즈를 풀면 정답과 설명이 나타납니다.

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 및 GloVe와 같은 초기 단어 임베딩의 주요 제한 사항은 무엇이었나요?
   • 컴퓨터에 저장할 수 없었습니다
   • 그들은 이미지에만 일했습니다
   • 훈련 데이터가 필요하지 않았습니다.
   • 각 단어에는 문맥에 관계없이 단일 고정 벡터가 있으므로 '은행'은 모든 곳에서 동일한 의미를 가집니다.
4. 현대적인 문맥 임베딩은 정적 단어 임베딩을 어떻게 개선합니까?
   • 표현에서 모든 숫자를 제거합니다.
   • 주변 문장에 따라 단어에 다른 벡터를 제공합니다.
   • 각 단어의 첫 글자만 포함됩니다.
   • 모든 단어의 벡터를 동일하게 만듭니다.
5. Which documented default similarity method does Sentence Transformers use for embedding comparison?
   • 코사인 유사성
   • 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