Zero-shot Text Classiﬁcation The input and label texts are converted to sentences based on manually
written prompt templates. The predicted label is the one closest to the input text in the embedding
space. Take the sentiment classiﬁcation of movie reviews as an example, with the original input “I
enjoy watching it”, the label text is “it is an example of terrible/great movie review” and the input text
becomes “movie review: I enjoy watching it”.
Semantic Textual Similarity Given two text embeddings, we use the cosine function to measure
their semantic similarity. Since the absolute similarity scores do not enable an easy interpretation, the
evaluation is usually based on rank correlation coefﬁcients.
Text Clustering Standard clustering algorithms such as k-means can be applied straightforwardly.
Texts belonging to the same category are expected to be close in the embedding space.
For tasks other than zero-shot text classiﬁcation and retrieval, we use the query embeddings by
default.
E5