CoT-BERT：通过思维链增强无监督句子表示
CoT-BERT: Enhancing Unsupervised Sentence Representation through Chain-of-Thought
AI总结：
针对无监督句子表示学习的瓶颈与外部组件引入带来的问题，本文提出CoT-BERT，结合思维链推理、新型对比损失与模板去噪策略，仅靠预训练模型内在能力就超越了多种成熟基线。
摘要：
无监督句子表示学习旨在将输入句子转换为蕴含复杂语义信息的固定长度向量，同时无需依赖标注数据。对比学习与提示工程的突破极大推动了该领域的近期进展。尽管已有这些进步，该领域仍进入了瓶颈期，部分研究者因此引入外部组件来提升句子嵌入质量。这类整合虽有裨益，却让方案更复杂，也抬高了计算资源需求。针对这些挑战，本文提出CoT-BERT这一创新方法，它利用思维链推理的渐进式思考，挖掘BERT等预训练模型的潜在能力。此外，我们还设计了一种先进的对比学习损失函数，并提出了一种新颖的模板去噪策略。严格实验表明，CoT-BERT仅凭借预训练模型的内在优势，性能就超过了多种成熟基线模型。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
