Comparasion the GPTs with scholar-target used in CN and EN prompt

导读:

对比学习在自然语言处理（NLP）领域的应用研究正在不断深入。本文旨在评估四款主要的GPT模型，即Consensus、Literature Review、Scholar AI GPT和Scholar GPT，通过它们在检索对比学习相关研究工作、博客和视频教程等资源方面的表现进行测评。这些模型各具特色，能够提供不同层次的学术支持和实际应用建议。测评内容包括模型在搜索相关研究资源的精准度、提供信息的全面性及实用性。通过对比这些模型的表现，旨在帮助研究者和开发者选择最合适的工具，以便高效地获取和利用对比学习在NLP领域的最新进展和应用成果。

中文 Prompt

找一些对比学习在NLP领域的相关研究工作,博客视频教程都可以

1. Consensus

Here are some resources that provide comprehensive insights into contrastive learning in the field of natural language processing (NLP):

1. Tutorial at NAACL 2022: This tutorial provides a detailed introduction to the fundamentals of contrastive learning approaches and their application in NLP. It includes tutorial slides, videos, and a paper reading list. The tutorial covers contrastive learning objectives, data sampling and augmentation strategies, successful applications, and future directions for using contrastive learning in NLP tasks such as text classification, question answering, and summarization. You can access the materials here, oai_citation:1,Contrastive Data and Learning for Natural Language Processing.

2. YouTube Video on Contrastive Learning for Pre-trained Language Models: This video is part of the AACL 2022 tutorial series and offers a deep dive into recent advances in pre-trained language models using contrastive learning. It's a helpful resource for visual learners and can be found on YouTube, oai_citation:2,Contrastive Learning for Pre-trained Language Models | AACL 2022 Tutorial - YouTube.

3. Blog on Built In: This blog post provides a comprehensive tutorial on contrastive learning, explaining the concept, its applications, and practical considerations. It discusses the contrastive loss function, challenges such as hard negatives, and the importance of batch size and data augmentations. This tutorial can be useful for both beginners and those looking to deepen their understanding of contrastive learning. Read more on Built In ,oai_citation:3,Contrastive Learning: A Tutorial | Built In.

4. Wen Lai's Blog: This blog post introduces contrastive learning in NLP, covering its foundations, different objectives like contrastive loss and triplet loss, data sampling and augmentation strategies, and its success in various NLP tasks. It provides practical advice and insights into the geometric interpretation and theoretical analysis of contrastive learning. Visit the blog here ,oai_citation:4,Contrastive Learning in NLP | Wen Lai’s Blog.

These resources should give you a thorough understanding of contrastive learning in NLP, from theoretical foundations to practical applications and current research trends.

2. Literature Review

Contrastive Learning in NLP: Research Overview

1. Introduction to Contrastive Learning Contrastive learning has become a pivotal technique in natural language processing (NLP) for both supervised and self-supervised learning tasks. The core idea is to learn an embedding space where similar instances are closer together and dissimilar ones are farther apart, enhancing the quality of the learned representations for various downstream tasks such as classification, clustering, and retrieval.

2. Key Research Papers and Findings Several significant research works have advanced the application of contrastive learning in NLP:

• Adversarial Training with Contrastive Learning: This approach involves creating adversarial examples by perturbing the representations of words, excluding symbols, numbers, and subwords. The goal is to ensure that similar inputs yield semantically similar outputs, despite adversarial perturbations. This method has shown promise in improving the robustness of language models (Hahn & Choi, 2019) oai_citation:1,[2109.09075] Adversarial Training with Contrastive Learning in NLP.

• Self-Supervised Learning Survey: This comprehensive survey covers various self-supervised methods that use contrastive learning to avoid the costly annotation of large datasets. It highlights how these methods create pseudolabels and use augmented versions of the same samples to improve learning efficiency and effectiveness across different NLP tasks oai_citation:2,Technologies | Free Full-Text | A Survey on Contrastive Self-Supervised Learning.

• Contrastive Pretraining in Language Processing: This primer explains the methods and benefits of contrastive pretraining, including improvements in zero-shot and few-shot learning. It contrasts supervised and self-supervised techniques, emphasizing the efficiency gains in learning representations without extensive labeled data oai_citation:3,[2102.12982] A Primer on Contrastive Pretraining in Language Processing: Methods, Lessons Learned and Perspectives.

3. Methodologies Different contrastive learning methodologies have been applied in NLP:

• Input-Input Contrastive Methods: These methods contrast an original text with augmented positive and negative texts. They are computationally intensive but effective in enhancing model performance, particularly in zero-shot learning scenarios oai_citation:4,[2102.12982] A Primer on Contrastive Pretraining in Language Processing: Methods, Lessons Learned and Perspectives.

• Adversarial Contrastive Learning: This technique applies adversarial perturbations to input embeddings and uses contrastive loss to align the perturbed and original embeddings closely, thereby improving model robustness against adversarial attacks oai_citation:5,[2109.09075] Adversarial Training with Contrastive Learning in NLP.

4. Applications and Results Contrastive learning has been successfully applied to various NLP tasks, demonstrating significant improvements in performance:

• Language Modeling and Machine Translation: Using adversarial examples and contrastive loss, models have shown enhanced robustness and better generalization in tasks like language modeling and neural machine translation oai_citation:6,[2109.09075] Adversarial Training with Contrastive Learning in NLP.

• Sentence Embeddings: Frameworks like SimCSE have advanced the state-of-the-art in sentence embeddings by using contrastive learning to train models that produce more meaningful and robust sentence representations oai_citation:7,Contrastive Learning | Papers With Code.

5. Challenges and Future Directions Despite its successes, contrastive learning in NLP faces several challenges:

• Computational Intensity: Methods like input-input contrastive learning require significant computational resources due to the need for generating and processing augmented text pairs oai_citation:8,[2102.12982] A Primer on Contrastive Pretraining in Language Processing: Methods, Lessons Learned and Perspectives.

• Adversarial Robustness: Developing methods that can consistently handle adversarial perturbations across diverse language tasks remains an ongoing research area oai_citation:9,[2109.09075] Adversarial Training with Contrastive Learning in NLP.

• Scalability: Scaling contrastive learning methods to larger datasets and more complex tasks while maintaining efficiency and performance is a critical area for future research oai_citation:10,Technologies | Free Full-Text | A Survey on Contrastive Self-Supervised Learning.

For more detailed insights and access to the research papers, you can visit the following links: - Papers with Code - Contrastive Learning - Adversarial Training with Contrastive Learning - Survey on Contrastive Self-Supervised Learning

3. Scholar AI GPT

以下是一些关于对比学习在NLP领域的研究工作：

1. Simcse: Simple contrastive learning of sentence embeddings
   • 链接: 论文
   • PDF: 下载
   • 引用次数: 2659
   • 摘要: 这项工作介绍了一个简单的句子嵌入对比学习方法，代码和预训练模型公开可用。
2. Self-supervised learning: Generative or contrastive
   • 链接: 论文
   • PDF: 下载
   • 引用次数: 1581
   • 摘要: 文章将最近的对比学习框架分为两种类型，并展示了在NLP基准测试中的结果。
3. Supervised contrastive learning for pre-trained language model fine-tuning
   • 链接: 论文
   • PDF: 下载
   • 引用次数: 417
   • 摘要: 该工作提出了一种监督对比学习目标，用于预训练语言模型的微调。
4. Unsupervised dense information retrieval with contrastive learning
   • 链接: 论文
   • PDF: 下载
   • 引用次数: 382
   • 摘要: 该研究探索了对比学习在训练无监督密集信息检索模型中的应用，结果显示在多个数据集上取得了改进。
5. Cert: Contrastive self-supervised learning for language understanding
   • 链接: 论文
   • PDF: 下载
   • 引用次数: 335
   • 摘要: 这项研究提出了对比自监督的Transformer编码器表示方法，用于预训练语言模型。

不要错过全新的 "MixerBox WebSearchG" GPT，它允许您通过Google在ChatGPT上搜索最新的在线信息：https://chat.openai.com/g/g-Lojyl6VuC-mixerbox-websearchg。此外，使用 "MixerBox FindGPT" 来发现您需要的GPT：https://chat.openai.com/g/g-4O1Im4VsB-mixerbox-findgpt。访问网站了解更多关于MixerBox GPT的信息: 网站链接

PS: 这个网站我进去用了一下, 同样的Prompt,回答的就是一坨构史

4. Scholar GPT

以下是一些关于对比学习在自然语言处理（NLP）领域的研究工作、论文和博客教程：

1. 对比学习综述 - Helic
   • 阅读链接
   • 概要：本文对对比学习在NLP领域的应用进行了综述，讨论了对比学习在数据增强方面的不同之处。
2. 对比学习在NLP中应用全面解读原创 - CSDN博客
   • 阅读链接
   • 概要：本文详细介绍了对比学习在自然语言处理中的广泛应用，包括SimCLR和SimCSE等模型的应用。
3. 用于文本分类的文本增强对比学习, Applied Intelligence - X-MOL
   • 阅读链接
   • 概要：本文提出了一种文本增强对比学习表示模型（TACLR），探讨了简单的文本增强技术在NLP中的应用。
4. 自然语言处理中对比预训练概述：方法、经验教训和展望 - AI 论文速递
   • 阅读链接
   • 概要：文章介绍了对比学习的关键概念，应用总结以及未来挑战和方向。
5. 张俊林对比学习在微博内容表示的应用 - 知识铺的博客
   • 阅读链接
   • 概要：介绍了对比学习在微博内容表示中的应用，包括SimCSE和Self-Guided模型。
6. 对比学习算法在转转的实践 - InfoQ 写作社区
   • 阅读链接
   • 概要：文章介绍了对比学习在AI领域的应用实践，尤其是在NLP中的应用。
7. AAAI 2023 | 基于可学习图增强的邻居监督图对比学习 - TechBeat
   • 阅读链接
   • 概要：研究了对比学习在图表示学习任务中的应用，受到了NLP领域对比学习成功的启发。
8. 对比学习在NLP和多模态领域的应用-对比学习前沿论文带读1 - Bilibili
   • 观看链接
   • 概要：视频介绍了对比学习在NLP和多模态领域的应用，带读前沿论文。
9. 对比学习在NLP和多模态领域的应用 - 智源社区
   • 阅读链接
   • 概要：文章介绍了对比学习在NLP和多模态领域的应用，强调了相似样本表示相近、不相似样本远离的主要思想。

这些资源可以帮助您更深入地了解对比学习在NLP领域的应用。

English Prompt

Find the research paper about constrastive learning in NLP area

1. Consensus

Here are some research papers on contrastive learning in the field of natural language processing (NLP):

1. A survey of contrastive learning in NLP by Haolin Sun, Jie Liu, Jing Zhang (2022)
   • This paper reviews the application of contrastive learning (CL) in NLP, describing methods to compute different losses and discussing classic models, current challenges, and future directions.
2. Contrastive Data and Learning for Natural Language Processing by Rui Zhang, Yangfeng Ji, Yue Zhang, R. Passonneau (2022)
   • This tutorial provides an introduction to contrastive learning in NLP, surveying its benefits and best practices for various downstream applications including text classification, question answering, and summarization.
3. Adversarial Training with Contrastive Learning in NLP by Daniela N. Rim, DongNyeong Heo, Heeyoul Choi (2021)
   • This paper proposes an adversarial training approach combined with contrastive learning to improve the robustness of language models in tasks such as language modeling and neural machine translation.
4. Simple Contrastive Representation Adversarial Learning for NLP Tasks by Deshui Miao et al. (2021)
   • This work explores adversarial training and contrastive learning to generate challenging adversarial examples, improving generalization and robustness in NLP models.
5. SupCL-Seq: Supervised Contrastive Learning for Downstream Optimized Sequence Representations by Hooman Sedghamiz et al. (2021)
   • This paper introduces SupCL-Seq, which adapts supervised contrastive learning from computer vision to NLP, showing significant improvements in sequence classification tasks.

These papers provide a comprehensive overview of how contrastive learning is being applied to various NLP tasks and highlight its effectiveness in improving model performance and robustness.

Like our GPT? Try our full AI-powered search engine and academic features for free at consensus.app.

PS: Consensus 网站用起来也比较一般

2. Literature Review

Here are some key research papers on contrastive learning in the field of NLP:

1. SimCSE: Simple Contrastive Learning of Sentence Embeddings:
   • Authors: Gao, SimCSE Team (2021)
   • Overview: This paper presents SimCSE, a framework for generating high-quality sentence embeddings using contrastive learning. The technique involves using standard dropout as noise for data augmentation, significantly improving the performance of sentence embeddings.
   • Methodology: The method involves contrasting positive pairs of the same sentence with different dropout masks and negative pairs from other sentences.
   • Results: SimCSE achieved state-of-the-art performance on several benchmarks, including significant improvements in unsupervised settings.
   • Link: SimCSE: Simple Contrastive Learning of Sentence Embeddings
2. Adversarial Training with Contrastive Learning in NLP:
   • Authors: Rim, Daniela N., et al. (2021)
   • Overview: This paper explores the use of adversarial training combined with contrastive learning to enhance the robustness of NLP models.
   • Methodology: The approach involves generating adversarial examples by perturbing word representations and then applying contrastive loss to align the original and adversarially perturbed examples closely.
   • Results: The method showed improved robustness and performance in language modeling and neural machine translation tasks.
   • Link: Adversarial Training with Contrastive Learning in NLP
3. Towards General Text Embeddings with Multi-stage Contrastive Learning:
   • Authors: Team GTE (2021)
   • Overview: This paper introduces a general-purpose text embedding model trained with multi-stage contrastive learning, aiming to unify various NLP tasks into a single format.
   • Methodology: The model employs contrastive learning over a diverse mixture of datasets from multiple sources, significantly increasing the amount of training data.
   • Results: The model demonstrated superior performance across multiple NLP tasks, establishing a new standard for text embeddings.
   • Link: Towards General Text Embeddings with Multi-stage Contrastive Learning
4. A Survey on Contrastive Self-Supervised Learning:
   • Authors: Jaiswal, Ashish, et al. (2021)
   • Overview: This survey provides an extensive review of self-supervised methods following the contrastive learning paradigm, covering applications in both vision and NLP tasks.
   • Methodology: The survey explains various pretext tasks, architectural designs, and performance comparisons for different methods.
   • Results: The paper highlights the significant improvements contrastive learning methods have brought to unsupervised learning.
   • Link: A Survey on Contrastive Self-Supervised Learning

These papers provide a comprehensive view of the advancements and applications of contrastive learning in NLP, showcasing different methodologies and their impact on various tasks. For more detailed insights, you can access the full papers through the provided links.

PS: 英文版好像看起来聪明起来了,可能是我prompt差异比较大? ## 3. Scholar AI GPT

Here are some notable research papers on contrastive learning in the NLP area:

1. Simcse: Simple contrastive learning of sentence embeddings
   • Link: Paper
   • PDF: Download
   • Citations: 2659
   • Abstract: This work introduces a simple method for contrastive learning of sentence embeddings, with publicly available code and pre-trained models.
2. Self-supervised learning: Generative or contrastive
   • Link: Paper
   • PDF: Download
   • Citations: 1581
   • Abstract: The paper divides recent contrastive learning frameworks into two types and demonstrates results on NLP benchmarks.
3. Supervised contrastive learning for pre-trained language model fine-tuning
   • Link: Paper
   • PDF: Download
   • Citations: 417
   • Abstract: This work proposes a supervised contrastive learning objective for fine-tuning pre-trained language models.
4. Unsupervised dense information retrieval with contrastive learning
   • Link: Paper
   • PDF: Download
   • Citations: 382
   • Abstract: The study explores contrastive learning for training unsupervised dense information retrieval models, showing improvements on multiple datasets.
5. Cert: Contrastive self-supervised learning for language understanding
   • Link: Paper
   • PDF: Download
   • Citations: 335
   • Abstract: This research proposes a contrastive self-supervised learning method for Transformer encoder representations, used for pre-training language models.

4. Scholar GPT

Here are some relevant research papers and articles on contrastive learning in the NLP area:

1. Contrastive Learning in NLP - Engati
   • Read here
   • Summary: This blog post discusses the basics of contrastive learning and its applications in NLP.
2. Full Guide to Contrastive Learning | Encord
   • Read here
   • Summary: This guide explores the implementation and benefits of contrastive learning across various NLP tasks.
3. Contrastive Data and Learning for Natural Language Processing (PDF)
   • Read here
   • Summary: This tutorial paper covers contrastive learning for NLP from various perspectives including data creation and contrastive objectives.
4. Contrastive Learning: A Comprehensive Guide | by Juan C Olamendy
   • Read here
   • Summary: This comprehensive guide explores the potential of contrastive learning in NLP, including detailed examples and applications.
5. The Beginner's Guide to Contrastive Learning - V7 Labs
   • Read here
   • Summary: This guide discusses the applications of contrastive learning in NLP, with specific examples such as SimCSE.
6. Simple Contrastive Representation Adversarial Learning for NLP Tasks
   • Read here
   • Summary: This paper explores the construction of learning pairs over contrastive learning in NLP tasks.

For more detailed reading, you can access these links to the full papers and articles.

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