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.

通过利用大型语言模型的生成和评估能力，我们提出了 SemCSR，一种语义感知的对比句子表示框架，可以自动构建高质量的 NLI 风格语料库，并将生成的句子对纳入对比句子表示模型的学习，实验证明了我们提出的框架在使用大型语言模型学习更好的句子表示方面的有效性。