Papers with Contrastive learning
Contrastive Data and Learning for Natural Language Processing (2022.naacl-tutorials)
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| Challenge: | Current NLP models heavily rely on effective representation learning algorithms. |
| Approach: | This tutorial introduces contrastive learning and provides an introduction to the techniques. |
| Outcome: | This tutorial provides an introduction to the fundamentals of contrastive learning approaches and the theory behind them. |
SKICSE: Sentence Knowable Information Prompted by LLMs Improves Contrastive Sentence Embeddings (2024.naacl-short)
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| Challenge: | Experimental results show that our model outperforms the previous state-of-the-art model GloVe on STS tasks. |
| Approach: | They propose a simple and effective prompt template that is able to obtain the knowable information of input sentences from LLMs. |
| Outcome: | The proposed model outperforms the previous state-of-the-art model PromptBERT on STS tasks. |
A Sentence is Worth 128 Pseudo Tokens: A Semantic-Aware Contrastive Learning Framework for Sentence Embeddings (2022.findings-acl)
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| Challenge: | Existing approaches to contrastive learning are heavily affected by superficial features like sentence length and syntax. |
| Approach: | They propose a semantic-aware contrastive learning framework for sentence embeddings that explores the pseudo-token space representation of a sentence while eliminating the impact of superficial features such as sentence length and syntax. |
| Outcome: | The proposed framework outperforms the state-of-the-art on six standard semantic textual similarity tasks while maintaining an additional queue to store the representation of sentence embeddings. |
An End-to-End Contrastive Self-Supervised Learning Framework for Language Understanding (2022.tacl-1)
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| Challenge: | Existing approaches to learning data representations using contrastive learning perform data augmentation and contrastive training separately. |
| Approach: | They propose a framework that performs data augmentation and contrastive learning end-to-end . they propose to combine data augmented with text encoders to optimize for contrastive training . |
| Outcome: | Experiments on GLUE and Gururangan datasets show the proposed framework is effective in NLP. |
Label Anchored Contrastive Learning for Language Understanding (2022.naacl-main)
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| Challenge: | a novel approach to contrastive learning for language understanding is not fully explored . contrastive training has been widely applied to self-supervised representation learning . |
| Approach: | They propose a label anchored contrastive learning approach for language understanding using a class label. |
| Outcome: | The proposed approach improves on GLUE and CLUE benchmarks by 4.1% compared to the state-of-the-art approaches . the proposed approach also improves under the few-shot and data imbalance settings . |
Cyclical Contrastive Learning Based on Geodesic for Zero-shot Cross-lingual Spoken Language Understanding (2024.findings-acl)
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| Challenge: | zero-shot cross-lingual SLU is a challenging task in low-resource languages . a lack of labeled training data makes it difficult to align representations of similar sentences . |
| Approach: | They propose a framework that uses cyclical contrastive learning to achieve consistency between languages . they propose to use geodesic to measure the similarity to construct positive and negative pairs . |
| Outcome: | The proposed framework achieves state-of-the-art performance on multiATIS++ and MTOP datasets. |
Trainable Hard Negative Examples in Contrastive Learning for Unsupervised Abstractive Summarization (2024.findings-eacl)
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| Challenge: | Existing methods for contrastive learning rely on manual negative examples and are poor in quality and adaptability during training. |
| Approach: | They propose a framework that learns trainable negative examples for contrastive learning in unsupervised abstractive summarization by combining a negative example network and a representation network. |
| Outcome: | The proposed approach eliminates the need for manual negative example design and improves on two benchmark datasets. |
CReSE: Benchmark Data and Automatic Evaluation Framework for Recommending Eligibility Criteria from Clinical Trial Information (2024.findings-eacl)
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| Challenge: | Eligibility criteria (EC) are defined as a set of conditions an individual must meet to participate in a clinical trial. |
| Approach: | They propose to recommend EC based on clinical trial information, including trial titles, and introduce an automatic evaluation framework to assess clinical validity of the EC recommendation model. |
| Outcome: | The proposed model outperforms existing language models pre-trained on the biomedical domain in EC clustering. |
Improved Universal Sentence Embeddings with Prompt-based Contrastive Learning and Energy-based Learning (2022.findings-emnlp)
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| Challenge: | Existing contrastive methods for learning universal sentence embeddings have limitations due to their over-parameterization and poor performance under domain shift settings. |
| Approach: | They propose to integrate an Energy-based Hinge loss to enhance the pairwise discriminative power of contrastive learning for sentence embeddings by combining PLMs with energy-based learning. |
| Outcome: | Empirical results show that the proposed method improves on seven standard semantic textual similarity tasks and a domain-shifted STS task. |
InfoCSE: Information-aggregated Contrastive Learning of Sentence Embeddings (2022.findings-emnlp)
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| Challenge: | Existing studies on contrastive learning for sentence embeddings are weak . researchers have started to use contrastive training to learn better unsupervised sentences. |
| Approach: | They propose an information-aggregated contrastive learning framework for learning unsupervised sentence embeddings. |
| Outcome: | The proposed framework outperforms SimCSE on several benchmark datasets w.r.t the semantic text similarity task. |
Contrastive Learning of Sentence Embeddings from Scratch (2023.emnlp-main)
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| Challenge: | Existing approaches to learn sentence embeddings with unlabeled data are limited due to copyright restrictions, data distribution issues, and messy formats. |
| Approach: | They propose a contrastive learning framework that trains sentence embeddings with synthetic data. |
| Outcome: | The proposed framework produces positive and negative annotations given unlabeled sentences and generates sentences along with their corresponding annotations from scratch. |
Exploring the Impact of Negative Samples of Contrastive Learning: A Case Study of Sentence Embedding (2022.findings-acl)
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| Challenge: | Unsupervised contrastive learning is emerging as a powerful technique for extracting knowledge from unlabeled data. |
| Approach: | They propose a momentum contrastive learning model with negative sample queue for sentence embedding with a simulated model with EMA update mechanism. |
| Outcome: | The proposed model achieves a Spearman’s correlation of 77.27% on the semantic text similarity task and a maximum traceable distance metric. |
ConRAS: Contrastive In-context Learning Framework for Retrieval-Augmented Summarization (2026.findings-eacl)
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| Challenge: | Despite progress in natural language processing, the potential of contrastive learning remains unexplored. |
| Approach: | They propose a framework that injects contrastive objectives into in-context learning-based retrieval-augmented summarization. |
| Outcome: | The proposed framework outperforms state-of-the-art retrieval-augmented methods on three summarization benchmarks showing that it can distinguish between positive and negative samples without parameter updates. |
Contrastive Learning with Keyword-based Data Augmentation for Code Search and Code Question Answering (2023.eacl-main)
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| Challenge: | Recent work on code search proposes data augmentation of queries for contrastive learning. |
| Approach: | They propose to augment query-code pairs with key words to preserve key words . they use keyDAC to fine-tune various pre-trained language models . |
| Outcome: | The proposed approach outperforms the current state-of-the-art in code search and question answering tasks. |
CARFT: Boosting LLM Reasoning via Contrastive Learning with Annotated Chain-of-Thought-based Reinforced Fine-Tuning (2025.emnlp-main)
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| Challenge: | Existing methods to improve the reasoning performance of Large Language Models (LLMs) ignore annotated Chain-of-Thought (CoT) and incorporate unstable reasoning path sampling. |
| Approach: | They propose a Contrastive learning with annotated CoT-based Reinforced Fine-Tuning approach to enhance the reasoning performance of Large Language Models. |
| Outcome: | The proposed approach exploits annotated CoT and stabilizes the fine-tuning procedure by incorporating an additional unsupervised learning signal. |
Keywords and Instances: A Hierarchical Contrastive Learning Framework Unifying Hybrid Granularities for Text Generation (2022.acl-long)
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Mingzhe Li, XieXiong Lin, Xiuying Chen, Jinxiong Chang, Qishen Zhang, Feng Wang, Taifeng Wang, Zhongyi Liu, Wei Chu, Dongyan Zhao, Rui Yan
| Challenge: | Existing studies focus on contrastive learning on the instance level without discriminating the contribution of each word. |
| Approach: | They propose a hierarchical contrastive learning mechanism which can unify semantic meaning in the input text. |
| Outcome: | The proposed model outperforms baselines on storytelling, paraphrasing, dialogue generation, and storytelling tasks. |
A Simple Angle-based Approach for Contrastive Learning of Unsupervised Sentence Representation (2024.findings-emnlp)
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| Challenge: | a promising baseline SimCSE has made notable breakthroughs in unsupervised SRL . however, there is still room for designing a novel contrastive framework specifically targeted for SRL. |
| Approach: | They propose an angle-based similarity function for a contrastive objective and propose a new approach for SRL. |
| Outcome: | The proposed approach shows better training dynamics on SRL than the standard cosine similarity function. |
ESimCSE: Enhanced Sample Building Method for Contrastive Learning of Unsupervised Sentence Embedding (2022.coling-1)
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| Challenge: | a new method for learning unsupervised sentence embeddings is proposed . unsup-SimCSE is biased because of the length information encoded into the sentence embeds . |
| Approach: | They propose a new unsupervised sentence embedding method that uses dropout to obtain positive pairs from a pre-trained Transformer encoder. |
| Outcome: | The proposed method outperforms the state-of-the-art unsup-SimCSE on a STS task. |
Improving Knowledge Graph Completion with Generative Hard Negative Mining (2023.findings-acl)
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| Challenge: | Existing methods for knowledge graph completion (KGC) use generative methods with a self-information-enhanced training strategy to generate high-quality negatives. |
| Approach: | They propose to leverage a sequence-to-sequence architecture to generate high-quality hard negatives from the same decoding distributions as the anchor. |
| Outcome: | The proposed method produces high-quality negatives with good hardness and diversity on three KGC benchmarks. |
OssCSE: Overcoming Surface Structure Bias in Contrastive Learning for Unsupervised Sentence Embedding (2023.emnlp-main)
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Zhan Shi, Guoyin Wang, Ke Bai, Jiwei Li, Xiang Li, Qingjun Cui, Belinda Zeng, Trishul Chilimbi, Xiaodan Zhu
| Challenge: | Recent studies show that contrastive learning is effective in sentence representation learning . but, the surface structure bias is a problem in the current model . |
| Approach: | They propose to combine a sentence with a sub-semantic sentence to investigate the surface structure bias. |
| Outcome: | The proposed model achieves state-of-the-art on standard semantic textual similarity tasks using different pre-trained backbones. |
Contrastive Learning with Prompt-derived Virtual Semantic Prototypes for Unsupervised Sentence Embedding (2022.findings-emnlp)
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| Challenge: | Recent studies focus on instance-wise contrastive learning, attempting to construct positive pairs with textual data augmentation. |
| Approach: | They propose a novel Contrastive learning method with Prompt-derived Virtual semantic prototypes that constructs virtual semantic prototype to each instance and derives negative prototypes by using the negative form of the prompts. |
| Outcome: | The proposed method performs on semantic textual similarity, transfer, and clustering tasks compared to baselines. |
Clustering-Aware Negative Sampling for Unsupervised Sentence Representation (2023.findings-acl)
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| Challenge: | Using clustering-aware learning, in-batch negatives are often ignored in sentence representation learning. |
| Approach: | They propose a method that integrates cluster information into contrastive learning for unsupervised sentence representation learning. |
| Outcome: | The proposed method compares favorably with baselines on semantic textual similarity tasks. |
Dynamic Energy-Based Contrastive Learning with Multi-Stage Knowledge Verification for Event Causality Identification (2025.emnlp-main)
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| Challenge: | Existing methods for event causal identification rely on rule-based or random sampling strategies, which introduce spurious causal positives. |
| Approach: | They propose an ECI method enhanced by Dynamic Energy-based Contrastive Learning with multi-stage knowledge verification which generates high-quality contrastive samples and effectively suppresses spurious causal disturbances. |
| Outcome: | The proposed method outperforms state-of-the-art methods on two benchmarks. |
Beyond Contrastive Learning: A Variational Generative Model for Multilingual Retrieval (2023.acl-long)
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| Challenge: | Contrastive learning is the dominant paradigm for learning text representations from parallel text, but finding negative examples can be expensive in terms of compute or manual effort. |
| Approach: | They propose a generative model for learning multilingual text embeddings which encourages source separation in multilingual contexts by an approximation. |
| Outcome: | The proposed model outperforms both a strong contrastive and generative baseline on a suite of tasks including semantic similarity, bitext mining, and cross-lingual question retrieval. |
Improving Contrastive Learning of Sentence Embeddings from AI Feedback (2023.findings-acl)
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| Challenge: | Existing methods to learn sentence embeddings with rich semantics are limited due to the discrete nature of natural language. |
| Approach: | They propose to use AI feedback to improve contrastive learning of sentence embeddings by combining human feedback and AI feedback. |
| Outcome: | The proposed method achieves state-of-the-art performance on several semantic textual similarity and transfer learning tasks compared to other unsupervised and supervised contrastive learning methods. |
CODER: An efficient framework for improving retrieval through COntextual Document Embedding Reranking (2022.emnlp-main)
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| Challenge: | Contextual document embedding reranking is an efficient and efficient retrieval framework. |
| Approach: | They propose a highly efficient retrieval framework that uses contextual document embedding reranking to incorporate ranking context into training. |
| Outcome: | The proposed framework reduces the computational overhead of a first-stage method and can be used as stand-alone retrieval models. |
HS-GC: Holistic Semantic Embedding and Global Contrast for Effective Text Clustering (2024.lrec-main)
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| Challenge: | In this paper, we introduce Holistic Semantic Embedding and Global Contrast (HS-GC) to learn the instance- and cluster-level representations. |
| Approach: | They propose a novel loss function that exploits different layers of semantic information in a deep neural network to provide a more holistic semantic text representation. |
| Outcome: | The proposed model outperforms the state-of-the-art model on five text datasets and improves clustering accuracy of 5.9% and 3.2% on the StackOverflow and TREC datasets. |
Prompt Tuning Pushes Farther, Contrastive Learning Pulls Closer: A Two-Stage Approach to Mitigate Social Biases (2023.acl-long)
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| Challenge: | Existing debiasing techniques use Counterfactual Data Augmentation (CDA) to balance the training corpus, but this technique slightly modifies the original corpus limiting the representation distance between different demographic groups. |
| Approach: | They propose a two-stage debiasing model using Contrastive learning with Continuous Prompt Augmentation to mitigate social biases in PLMs’ encoding. |
| Outcome: | The proposed model outperforms baselines in terms of debiasing performance while maintaining the language modeling capability of PLMs. |
SLIDE: A Framework Integrating Small and Large Language Models for Open-Domain Dialogues Evaluation (2024.findings-acl)
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| Challenge: | Existing approaches to evaluate open domain dialogues have a one-to-many problem . existing approaches lack commonsense reasoning biases and perform poorly in domain-specific scenarios. |
| Approach: | They propose a framework that leverages both a small, specialised model and LLMs for the evaluation of open-domain dialogues. |
| Outcome: | The proposed framework achieves state-of-the-art performance in both classification and evaluation tasks and exhibits better correlation with human judgements. |
SDA: Simple Discrete Augmentation for Contrastive Sentence Representation Learning (2024.lrec-main)
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| Challenge: | Existing methods for data augmentation have not been well explored. |
| Approach: | They propose to use punctuation insertion, modal verbs, and double negation to produce diverse forms of sentences. |
| Outcome: | The proposed methods perform better on diverse datasets with semantic similarity and standard negation. |