# CoT-BERT: Enhancing Unsupervised Sentence Representation through Chain-of-Thought

arXiv: https://arxiv.org/abs/2309.11143 · HTML: https://mlsanity.com/paper/2309.11143/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Placed in Prompting and chain-of-thought reasoning, one of 17 directions in LLM reasoning.
- Builds on 25 of its 37 references that are in the map.
- First posted on arXiv 20 Sep 2023.

## Placed in

- LLM reasoning / [Prompting and chain-of-thought reasoning](https://mlsanity.com/lr/prompting-and-chain-of-thought-reasoning/)

## Builds on (most cited first, 25 of 25)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Chain-of-Thought Prompting Elicits Reasoning in Large Language Models](https://mlsanity.com/paper/2201.11903/) (2022)
- [Representation Learning with Contrastive Predictive Coding](https://mlsanity.com/paper/1807.03748/) (2018)
- [PaLM: Scaling Language Modeling with Pathways](https://mlsanity.com/paper/2204.02311/) (2022)
- [OPT: Open Pre-trained Transformer Language Models](https://mlsanity.com/paper/2205.01068/) (2022)
- [SimCSE: Simple Contrastive Learning of Sentence Embeddings](https://mlsanity.com/paper/2104.08821/) (2021)
- [SemEval-2017 Task 1: Semantic Textual Similarity - Multilingual and Cross-lingual Focused Evaluation](https://mlsanity.com/paper/1708.00055/) (2017)
- [SentEval: An Evaluation Toolkit for Universal Sentence Representations](https://mlsanity.com/paper/1803.05449/) (2018)
- [ConSERT: A Contrastive Framework for Self-Supervised Sentence Representation Transfer](https://mlsanity.com/paper/2105.11741/) (2021)
- [Whitening Sentence Representations for Better Semantics and Faster Retrieval](https://mlsanity.com/paper/2103.15316/) (2021)
- [DiffCSE: Difference-based Contrastive Learning for Sentence Embeddings](https://mlsanity.com/paper/2204.10298/) (2022)
- [An Unsupervised Sentence Embedding Method by Mutual Information Maximization](https://mlsanity.com/paper/2009.12061/) (2020)
- [PromptBERT: Improving BERT Sentence Embeddings with Prompts](https://mlsanity.com/paper/2201.04337/) (2022)
- [ESimCSE: Enhanced Sample Building Method for Contrastive Learning of Unsupervised Sentence Embedding](https://mlsanity.com/paper/2109.04380/) (2021)
- [Debiased Contrastive Learning of Unsupervised Sentence Representations](https://mlsanity.com/paper/2205.00656/) (2022)
- [SNCSE: Contrastive Learning for Unsupervised Sentence Embedding with Soft Negative Samples](https://mlsanity.com/paper/2201.05979/) (2022)
- [Improved Universal Sentence Embeddings with Prompt-based Contrastive Learning and Energy-based Learning](https://mlsanity.com/paper/2203.06875/) (2022)
- [PCL: Peer-Contrastive Learning with Diverse Augmentations for Unsupervised Sentence Embeddings](https://mlsanity.com/paper/2201.12093/) (2022)
- [RankCSE: Unsupervised Sentence Representations Learning via Learning to Rank](https://mlsanity.com/paper/2305.16726/) (2023)
- [Alleviating Over-smoothing for Unsupervised Sentence Representation](https://mlsanity.com/paper/2305.06154/) (2023)
- [Ranking-Enhanced Unsupervised Sentence Representation Learning](https://mlsanity.com/paper/2209.04333/) (2022)
- [Contrastive Learning with Prompt-derived Virtual Semantic Prototypes for Unsupervised Sentence Embedding](https://mlsanity.com/paper/2211.03348/) (2022)

## Built on by (most cited first, 8 of 8)

- [Navigate through Enigmatic Labyrinth A Survey of Chain of Thought Reasoning: Advances, Frontiers and Future](https://mlsanity.com/paper/2309.15402/) (2023)
- [Simple Techniques for Enhancing Sentence Embeddings in Generative Language Models](https://mlsanity.com/paper/2404.03921/) (2024)
- [Advancing Semantic Textual Similarity Modeling: A Regression Framework with Translated ReLU and Smooth K2 Loss](https://mlsanity.com/paper/2406.05326/) (2024)
- [Pcc-tuning: Breaking the Contrastive Learning Ceiling in Semantic Textual Similarity](https://mlsanity.com/paper/2406.09790/) (2024)
- [CSE-SFP: Enabling Unsupervised Sentence Representation Learning via a Single Forward Pass](https://mlsanity.com/paper/2505.00389/) (2025)
- [CoDiEmb: A Collaborative yet Distinct Framework for Unified Representation Learning in Information Retrieval and Semantic Textual Similarity](https://mlsanity.com/paper/2508.11442/) (2025)
- [PublicAgent: Multi-Agent Design Principles From an LLM-Based Open Data Analysis Framework](https://mlsanity.com/paper/2511.03023/) (2025)
- [PoLi-RL: A Point-to-List Reinforcement Learning Framework for Conditional Semantic Textual Similarity](https://mlsanity.com/paper/2510.04080/) (2025)

## Abstract (arXiv)

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.