  Contrastive learning has become a popular approach in natural language
processing, particularly for the learning of sentence embeddings. However, the
discrete nature of natural language makes it difficult to ensure the quality of
positive and negative sample pairs generated through data augmentation methods.
Although supervised contrastive learning can produce more accurate sample pairs
with human feedback labels, it still lacks fine-grained training signals. In
this paper, we propose to improve \textbf{C}ontrastive \textbf{L}earning of
sentence embeddings from \textbf{AI} \textbf{F}eedback \textbf{(CLAIF)}. Our
method utilizes AI feedback from large pre-trained language models (LLMs) to
construct sample pairs with fine-grained sample similarity scores to improve
contrastive learning. Besides, we combine human feedback and AI feedback to
provide better supervision signals for supervised contrastive learning of
sentence embeddings. Experimental results show that our method achieves
state-of-the-art performance on several semantic textual similarity (STS) and
transfer learning tasks compared to other unsupervised and supervised
contrastive learning methods.
