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.