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 progress within this field, propelled by contrastive learning and prompt engineering, has significantly bridged the gap between unsupervised and supervised strategies. Nonetheless, the potential utilization of Chain-of-Thought, remains largely untapped within this trajectory. To unlock latent capabilities within pre-trained models, such as BERT, we propose a two-stage approach for sentence representation: comprehension and summarization. Subsequently, the output of the latter phase is harnessed as the vectorized representation of the input sentence. For further performance enhancement, we meticulously refine both the contrastive learning loss function and the template denoising technique for prompt engineering. Rigorous experimentation substantiates our method, CoT-BERT, transcending a suite of robust baselines without necessitating other text representation models or external databases.

翻译：无监督句子表示学习旨在将输入句子转换为富含复杂语义信息的定长向量，同时避免对标注数据的依赖。该领域近期在对比学习和提示工程的推动下取得了显著进展，极大地缩小了无监督方法与监督策略之间的差距。然而，思维链的潜在应用在这一研究路径中仍未得到充分开发。为解锁BERT等预训练模型的隐藏能力，我们提出了一种两阶段句子表示方法：理解与总结。随后，后一阶段的输出被用作输入句子的向量化表示。为进一步提升性能，我们精心优化了对比学习损失函数及提示工程的模板去噪技术。大量实验证明，我们的方法CoT-BERT在不依赖其他文本表示模型或外部数据库的情况下，超越了多种强基线方法。