Semantic-Aware Contrastive Sentence Representation Learning with Large
  Language Models
Huiming Wang Liying Cheng Zhaodonghui Li De Wen Soh Lidong Bing
Contrastive learning has been proven to be effective in learning better
sentence representations. However, to train a contrastive learning model, large
numbers of labeled sentences are required to construct positive and negative
pairs explicitly, such as those in natural language inference (NLI) datasets.
Unfortunately, acquiring sufficient high-quality labeled data can be both
time-consuming and resource-intensive, leading researchers to focus on
developing methods for learning unsupervised sentence representations. As there
is no clear relationship between these unstructured randomly-sampled sentences,
building positive and negative pairs over them is tricky and problematic. To
tackle these challenges, in this paper, we propose SemCSR, a semantic-aware
contrastive sentence representation framework. By leveraging the generation and
evaluation capabilities of large language models (LLMs), we can automatically
construct a high-quality NLI-style corpus without any human annotation, and
further incorporate the generated sentence pairs into learning a contrastive
sentence representation model. Extensive experiments and comprehensive analyses
demonstrate the effectiveness of our proposed framework for learning a better
sentence representation with LLMs.