  Unsupervised sentence representation learning aims to transform input
sentences into fixed-length vectors enriched with intricate semantic
information while obviating the reliance on labeled data. Recent strides within
this domain have been significantly propelled by breakthroughs in contrastive
learning and prompt engineering. Despite these advancements, the field has
reached a plateau, leading some researchers to incorporate external components
to enhance the quality of sentence embeddings. Such integration, though
beneficial, complicates solutions and inflates demands for computational
resources. In response to these challenges, this paper presents CoT-BERT, an
innovative method that harnesses the progressive thinking of Chain-of-Thought
reasoning to tap into the latent potential of pre-trained models like BERT.
Additionally, we develop an advanced contrastive learning loss function and
propose a novel template denoising strategy. Rigorous experimentation
demonstrates that CoT-BERT surpasses a range of well-established baselines by
relying exclusively on the intrinsic strengths of pre-trained models.
